fullseye

文献層: メカ設計(機械設計)

何のコーパスか: CAD 表現(パラメトリック・B-rep・CSG・SDF)、generative design・トポロジー最適化、DfAM・格子、公差解析・GD&T、機構合成・コンプライアント機構、歯車・軸受・ボルト、疲労・強度・FEA、材料選定、射出成形・板金の設計ルール、DFA/DFM、ロボット機構、逆設計(点群 → CAD)、スライス・工具経路。

Fullseye でなぜ要るか: 設計 → スライス → 印刷 → 検査 → 再設計 の閉ループ(printpath と SDF/CSG の族、進化エンジン)の設計側の知識。

来歴: OpenAlex から取得した論文メタデータ 2,240 本(2012 年以降、fetch_openalex_topical.py)を TF-IDF + k-means で 64 クラスタに階層化し(raptor の corpus2skill、2026-09-21)、各クラスタの要約を LLM(claude-haiku-4-5)が書いた。ここに載せるのはその要約と、代表論文の題名・著者・年・DOI だけ(メタデータは CC0。抄録は出版社の権利なので写さない)。要約は生成時の英語のまま。正直な限界: 文献は原理・選定・校正の研究が中心で、型番やデータシートの数値(製品知識)は薄い。

使い方(op に落とす): 各クラスタの「使う op」は、知識のテーマ → op の対応表(tools/gen_literature_notes.py の THEME_OPS、人が書いたもの)で引いた実在の op(生成時に出荷ノート 3,623 本の一覧と突き合わせ、無ければ止まる)。op の後ろはなぜ使うかと型。テーマに当たらないクラスタは「op に落ちていない」と書く(Fullseye に無い領域を隠さない)。読む順: 工程・部品の話題 → クラスタ → 使う op → op ノート(型の契約・実行できる例)→ 実装。末尾の「op → クラスタ」で逆に引ける。

目次

• point / shape / cloud(175 本)
• cad / parametric / design(88 本)
• fatigue / bolted / joints(206 本)
• optimization / topology / design(256 本)
• robot / robotic / control(239 本)
• additive / manufacturing / printing(318 本)
• injection / molding / fault(147 本)
• materials / review / design(811 本)

point / shape / cloud

175 本

This cluster covers computational techniques for processing, analyzing, and reconstructing 3D geometric structures from point clouds and shape data. It encompasses deep learning approaches, transformation algorithms, reverse engineering methodologies, and implicit representation methods for 3D geometry understanding and generation.

• Point Cloud Processing: Neural network architectures (Transformers, attention mechanisms) designed for irregular, unordered 3D point data
• 3D Shape Representation: Multiple encoding strategies including explicit meshes, implicit functions, signed distance fields, and constructive solid geometry
• Reverse Engineering Workflows: Multi-sensor acquisition, registration algorithms, and optimization techniques for reconstructing geometry from physical objects or scans
• Deep Geometry Learning: Feature extraction and learning paradigms adapted from 2D computer vision to handle 3D geometric irregularities
• Implicit Representations: Neural implicit functions and signed distance functions for efficient 3D shape synthesis and reconstruction

使う op(テーマ → op の対応表から。ノートで型と例を確かめてから使う):

• 点群 → 当てはめ → CAD(逆設計): voxel_grid_downsample(スキャン点群を等間隔に間引く、points → points)、statistical_outlier_removal(外れ点を落とす、points → points)、estimate_normals(法線(面の向き)、points → normals)、icp_point2plane(設計形状との位置合わせ、points × points × normals → pose)、register_fpfh(初期姿勢の無い位置合わせ、points × points → pose)、ransac_plane(平面プリミティブの当てはめ、points → primitive)、ransac_cylinder(円筒の当てはめ(穴・軸)、points × normals → primitive)、fit_cone(円錐、points → primitive)、poisson_lite(点群 → メッシュ、points → mesh)、chamfer_distance(設計との形状差、points × points → measurement)、hausdorff_distance(最悪の形状差、points × points → measurement)
• メッシュ・形状の計測と変換: mesh_area(表面積、mesh → measurement)、mesh_volume(体積、mesh → measurement)、vertex_curvature(曲率(角・フィレット)、mesh → curvature)、decimate_qem(間引き、mesh → mesh)、mesh_isotropic_remesh(等方リメッシュ、mesh → mesh)、mesh_to_voxel(メッシュ → 体積、mesh → voxel)、mesh_sample_points(メッシュ → 点群、mesh → points)、shape_index(局所形状の型、points → descriptor)、d2_distribution(形状の記述子(検索・比較)、points → descriptor)、procrustes_distance(位置・向き・大きさを除いた形の差、points × points → measurement)、shape_pca(同種部品の形のばらつき(統計形状モデル)、shapeset → shapemodel)
• パラメトリック形状(SDF / CSG): box_sdf(直方体の SDF、coordgrid → sdf)、cylinder_sdf(円筒の SDF、coordgrid → sdf)、capsule_sdf(カプセル、coordgrid → sdf)、plane_sdf(平面(切り欠き)、coordgrid → sdf)、sdf_union(和、sdf × sdf → sdf)、sdf_subtract(差(穴)、sdf × sdf → sdf)、sdf_intersect(積、sdf × sdf → sdf)、sdf_smooth_union(フィレットつきの和、sdf × sdf → sdf)、sdf_offset(肉厚のオフセット、sdf → sdf)、sdf_to_occupancy(SDF → 占有(スライスへ)、sdf → voxel)、grid_coords(評価格子、入力なし → coordgrid)、fit_superquadric(スーパー二次曲面の当てはめ(逆設計)、points → primitive)、fit_bspline_surface(自由曲面の当てはめ、image2d × image2d × image2d → bspline_surface)

clouds / point / building

This cluster covers techniques and methodologies for reconstructing three-dimensional building models and infrastructure from point cloud data acquired through LiDAR scanning and photogrammetry.

• 使う op: voxel_grid_downsample(スキャン点群を等間隔に間引く、points → points)、statistical_outlier_removal(外れ点を落とす、points → points)、estimate_normals(法線(面の向き)、points → normals)、icp_point2plane(設計形状との位置合わせ、points × points × normals → pose)、register_fpfh(初期姿勢の無い位置合わせ、points × points → pose)、ransac_plane(平面プリミティブの当てはめ、points → primitive)、ほか 5 個: ransac_cylinder、fit_cone、poisson_lite、chamfer_distance、hausdorff_distance
• 代表論文(新しい順 + いちばん古い 1 本):
  • Yujia Liu et al. (2024). Point2Building: Reconstructing buildings from airborne LiDAR point clouds. 10.1016/j.isprsjprs.2024.07.012
  • Yujia Liu et al. (2024). Point2CAD: Reverse Engineering CAD Models from 3D Point Clouds. 10.1109/cvpr52733.2024.00361
  • Yujia Liu et al. (2023). Point2CAD: Reverse Engineering CAD Models from 3D Point Clouds. 10.48550/arxiv.2312.04962
  • Zhaiyu Chen et al. (2022). Reconstructing compact building models from point clouds using deep implicit fields. 10.1016/j.isprsjprs.2022.09.017
  • Elena Camuffo et al. (2022). Recent Advancements in Learning Algorithms for Point Clouds: An Updated Overview. 10.3390/s22041357
  • Jack C.P. Cheng et al. (2013). Mapping between BIM models and 3d GIS city models of different levels of detail. W1024085964

signed / distance / reconstruction

This cluster covers signed distance field (SDF) representations and their application to 3D reconstruction and spatial mapping tasks.

• 使う op: box_sdf(直方体の SDF、coordgrid → sdf)、cylinder_sdf(円筒の SDF、coordgrid → sdf)、capsule_sdf(カプセル、coordgrid → sdf)、plane_sdf(平面(切り欠き)、coordgrid → sdf)、sdf_union(和、sdf × sdf → sdf)、sdf_subtract(差(穴)、sdf × sdf → sdf)、ほか 7 個: sdf_intersect、sdf_smooth_union、sdf_offset、sdf_to_occupancy、grid_coords、fit_superquadric、fit_bspline_surface
• 代表論文(新しい順 + いちばん古い 1 本):
  • Joseph D. Ortiz et al. (2022). iSDF: Real-Time Neural Signed Distance Fields for Robot Perception. 10.15607/rss.2022.xviii.012
  • Fabio Maria Carlucci et al. (2019). Domain Generalization by Solving Jigsaw Puzzles. 10.1109/cvpr.2019.00233
  • Helen Oleynikova et al. (2016). Signed Distance Fields: A Natural Representation for Both Mapping and Planning. 10.3929/ethz-a-010820134
  • Michael Zollhöfer et al. (2015). Shading-based refinement on volumetric signed distance functions. 10.1145/2766887
  • Matthew Klingensmith et al. (2015). Chisel: Real Time Large Scale 3D Reconstruction Onboard a Mobile Device using Spatially Hashed Signed Distance Fields. 10.15607/rss.2015.xi.040

csg / geometry / constructive

This cluster covers constructive solid geometry (CSG) techniques and geometric modeling approaches for representing, analyzing, and reconstructing 3D shapes.

• 使う op: mesh_area(表面積、mesh → measurement)、mesh_volume(体積、mesh → measurement)、vertex_curvature(曲率(角・フィレット)、mesh → curvature)、decimate_qem(間引き、mesh → mesh)、mesh_isotropic_remesh(等方リメッシュ、mesh → mesh)、mesh_to_voxel(メッシュ → 体積、mesh → voxel)、ほか 18 個: mesh_sample_points、shape_index、d2_distribution、procrustes_distance、shape_pca、box_sdf、cylinder_sdf、capsule_sdf、plane_sdf、sdf_union、sdf_subtract、sdf_intersect、sdf_smooth_union、sdf_offset、sdf_to_occupancy、grid_coords、fit_superquadric、fit_bspline_surface
• 代表論文(新しい順 + いちばん古い 1 本):
  • Maximilian Mews et al. (2024). Don’t Mesh with Me: Generating Constructive Solid Geometry Instead of Meshes by Fine-Tuning a Code-Generation LLM. 10.48550/arxiv.2411.15279
  • J. Chen et al. (2024). FR-CSG: Fast and Reliable Modeling for Constructive Solid Geometry. 10.1109/tvcg.2024.3481278
  • Paul Romano et al. (2024). Point containment algorithms for constructive solid geometry with unbounded primitives. 10.48550/arxiv.2406.13030
  • Zoë Marschner et al. (2023). Constructive Solid Geometry on Neural Signed Distance Fields. 10.1145/3610548.3618170
  • Yuanqi Li et al. (2023). Surface and Edge Detection for Primitive Fitting of Point Clouds. 10.1145/3588432.3591522
  • Helmi Ben Hmida et al. (2012). From quantitative spatial operator to qualitative spatial relation using Constructive Solid Geometry, logic rules and optimized 9-IM model: A semantic based approach. 10.1109/csae.2012.6272992

point / cloud / cultural

This cluster covers techniques for processing, analyzing, and reconstructing 3D point cloud data, with emphasis on applications in cultural heritage documentation, reverse engineering, and infrastructure monitoring.

• 使う op: voxel_grid_downsample(スキャン点群を等間隔に間引く、points → points)、statistical_outlier_removal(外れ点を落とす、points → points)、estimate_normals(法線(面の向き)、points → normals)、icp_point2plane(設計形状との位置合わせ、points × points × normals → pose)、register_fpfh(初期姿勢の無い位置合わせ、points × points → pose)、ransac_plane(平面プリミティブの当てはめ、points → primitive)、ほか 5 個: ransac_cylinder、fit_cone、poisson_lite、chamfer_distance、hausdorff_distance
• 代表論文(新しい順 + いちばん古い 1 本):
  • Д. И. Рухович et al. (2025). CAD-Recode: Reverse Engineering CAD Code From Point Clouds. 10.1109/iccv51701.2025.00914
  • Xi‐Lin Wang et al. (2025). From 2D CAD Drawings to 3D Parametric Models: A Vision-Language Approach. 10.1609/aaai.v39i8.32858
  • Mohammad Sadil Khan et al. (2024). CAD-SIGNet: CAD Language Inference from Point Clouds Using Layer-Wise Sketch Instance Guided Attention. 10.1109/cvpr52733.2024.00451
  • Keneni W. Tesema et al. (2023). Point Cloud Completion: A Survey. 10.1109/tvcg.2023.3344935
  • Jan Polák et al. (2023). From Structural Optimization Results to Parametric CAD Modeling—Automated, Skeletonization-Based Truss Recognition. 10.3390/app13095670
  • Kuang-Hua Chang (2012). A Review on Shape Engineering and Design Parameterization in Reverse Engineering. 10.5772/32419

implicit / shape / shapes

This cluster covers implicit neural representations for 3D shape modeling, generation, and reconstruction.

• 使う op: mesh_area(表面積、mesh → measurement)、mesh_volume(体積、mesh → measurement)、vertex_curvature(曲率(角・フィレット)、mesh → curvature)、decimate_qem(間引き、mesh → mesh)、mesh_isotropic_remesh(等方リメッシュ、mesh → mesh)、mesh_to_voxel(メッシュ → 体積、mesh → voxel)、ほか 18 個: mesh_sample_points、shape_index、d2_distribution、procrustes_distance、shape_pca、box_sdf、cylinder_sdf、capsule_sdf、plane_sdf、sdf_union、sdf_subtract、sdf_intersect、sdf_smooth_union、sdf_offset、sdf_to_occupancy、grid_coords、fit_superquadric、fit_bspline_surface
• 代表論文(新しい順 + いちばん古い 1 本):
  • J. Wang et al. (2024). Implicit Swept Volume SDF: Enabling Continuous Collision-Free Trajectory Generation for Arbitrary Shapes. 10.1145/3658181
  • Yujie Lu et al. (2024). Unsigned Orthogonal Distance Fields: An Accurate Neural Implicit Representation for Diverse 3D Shapes. 10.1109/cvpr52733.2024.01942
  • Junyan He et al. (2024). Geom-DeepONet: A point-cloud-based deep operator network for field predictions on 3D parameterized geometries. 10.1016/j.cma.2024.117130
  • Jia-Mu Sun et al. (2024). Recent advances in implicit representation-based 3D shape generation. 10.1007/s44267-024-00042-1
  • Ziya Erkoç et al. (2023). HyperDiffusion: Generating Implicit Neural Fields with Weight-Space Diffusion. 10.1109/iccv51070.2023.01315
  • Robert I. Saye (2014). High-order methods for computing distances to implicitly defined surfaces. 10.2140/camcos.2014.9.107

reverse / cad / cloud

This cluster covers reverse engineering methodologies applied to physical objects and mechanical systems, with emphasis on 3D scanning, CAD reconstruction, and digital modeling techniques.

• 使う op: voxel_grid_downsample(スキャン点群を等間隔に間引く、points → points)、statistical_outlier_removal(外れ点を落とす、points → points)、estimate_normals(法線(面の向き)、points → normals)、icp_point2plane(設計形状との位置合わせ、points × points × normals → pose)、register_fpfh(初期姿勢の無い位置合わせ、points × points → pose)、ransac_plane(平面プリミティブの当てはめ、points → primitive)、ほか 18 個: ransac_cylinder、fit_cone、poisson_lite、chamfer_distance、hausdorff_distance、box_sdf、cylinder_sdf、capsule_sdf、plane_sdf、sdf_union、sdf_subtract、sdf_intersect、sdf_smooth_union、sdf_offset、sdf_to_occupancy、grid_coords、fit_superquadric、fit_bspline_surface
• 代表論文(新しい順 + いちばん古い 1 本):
  • Binoy Debnath et al. (2025). Integrating Reverse Engineering for Digital Model Reconstruction and Remanufacturing of Mechanical Components: A Systematic Review. 10.3390/metrology5040066
  • P. Turek et al. (2024). Analysis of the Accuracy of CAD Modeling in Engineering and Medical Industries Based on Measurement Data Using Reverse Engineering Methods. 10.3390/designs8030050
  • Long Zheng et al. (2023). Reverse Engineering-Inspired Parametric 3D Geometry Model of Marine Propeller. 10.2478/pomr-2023-0037
  • Michal Fabián et al. (2022). Reverse Engineering and Rapid Prototyping in the Process of Developing Prototypes of Automotive Parts. 10.21062/mft.2022.084
  • Linlin Fan et al. (2022). A Reverse Modeling Method Based on CAD Model Prior and Surface Modeling. 10.3390/machines10100905
  • Atul Kumar et al. (2012). Industrial Application of Point Cloud / STL Data for Reverse Engineering. 10.2507/daaam.scibook.2012.38

image / radiance / views

This cluster covers neural radiance fields (NeRF) and advanced computer vision techniques for 3D scene understanding, pose estimation, and view synthesis.

• 代表論文(新しい順 + いちばん古い 1 本):
  • Haozhe Qi et al. (2024). HOISDF: Constraining 3D Hand-Object Pose Estimation with Global Signed Distance Fields. 10.1109/cvpr52733.2024.00989
  • Meng-Hao Guo et al. (2022). Attention mechanisms in computer vision: A survey. 10.1007/s41095-022-0271-y
  • Ben Mildenhall et al. (2021). NeRF. 10.1145/3503250
  • Hongyi Xu et al. (2021). H-NeRF: Neural Radiance Fields for Rendering and Temporal Reconstruction of Humans in Motion. 10.48550/arxiv.2110.13746
  • Yen-Chen Lin et al. (2021). iNeRF: Inverting Neural Radiance Fields for Pose Estimation. 10.1109/iros51168.2021.9636708
  • Mathieu Aubry et al. (2014). Seeing 3D Chairs: Exemplar Part-Based 2D-3D Alignment Using a Large Dataset of CAD Models. 10.1109/cvpr.2014.487

registration / point / cloud

This cluster covers point cloud registration techniques and their applications in 3D reconstruction, reverse engineering, and geospatial modeling.

• 使う op: voxel_grid_downsample(スキャン点群を等間隔に間引く、points → points)、statistical_outlier_removal(外れ点を落とす、points → points)、estimate_normals(法線(面の向き)、points → normals)、icp_point2plane(設計形状との位置合わせ、points × points × normals → pose)、register_fpfh(初期姿勢の無い位置合わせ、points × points → pose)、ransac_plane(平面プリミティブの当てはめ、points → primitive)、ほか 5 個: ransac_cylinder、fit_cone、poisson_lite、chamfer_distance、hausdorff_distance
• 代表論文(新しい順 + いちばん古い 1 本):
  • M. R. Mahendrini Fernando Ariyachandra et al. (2023). Leveraging railway topology to automatically generate track geometric information models from airborne LiDAR data. 10.1016/j.autcon.2023.105068
  • Su Yang et al. (2023). Three-Dimensional Point Cloud Semantic Segmentation for Cultural Heritage: A Comprehensive Review. 10.3390/rs15030548
  • Valeria Croce et al. (2021). From the Semantic Point Cloud to Heritage-Building Information Modeling: A Semiautomatic Approach Exploiting Machine Learning. 10.3390/rs13030461
  • George Yacout et al. (2021). Reverse Engineering: Investigation of Optimization Techniques in Point Clouds Registration. 10.1002/pamm.202000165
  • Simone Teruggi et al. (2020). A Hierarchical Machine Learning Approach for Multi-Level and Multi-Resolution 3D Point Cloud Classification. 10.3390/rs12162598
  • Enrique Valero et al. (2012). Automatic Method for Building Indoor Boundary Models from Dense Point Clouds Collected by Laser Scanners. 10.3390/s121216099

cad / parametric / design

88 本

This cluster covers parametric and generative design methodologies in CAD systems, emphasizing how design intent is captured through parameters, constraints, and automation. It addresses the integration of parametric modeling with AI/LLMs, design complexity management, and best practices for creating reusable, editable digital product models across engineering domains.

• Parametric modeling fundamentals: History-based parametric CAD enables design intent capture, making models editable and reusable; complexity and parameter relationships directly impact model maintainability
• Design-to-manufacturing automation: Generative AI and LLMs can bridge CAD systems with downstream manufacturing processes, reducing manual handoff and iteration cycles
• Model complexity metrics: Quantitative analysis of parametric model complexity correlates with perceived usability; high-complexity models resist reuse and redesign
• Domain-specific parametric systems: Knowledge-based parametric modeling (e.g., gear design, spine biomechanics) embeds engineering rules and constraints to automate specialized design tasks
• Integration with analysis tools: Parametric CAD/CAE environments unify design and simulation in a single workspace, enabling iterative design optimization

使う op(テーマ → op の対応表から。ノートで型と例を確かめてから使う):

• パラメトリック形状(SDF / CSG): box_sdf(直方体の SDF、coordgrid → sdf)、cylinder_sdf(円筒の SDF、coordgrid → sdf)、capsule_sdf(カプセル、coordgrid → sdf)、plane_sdf(平面(切り欠き)、coordgrid → sdf)、sdf_union(和、sdf × sdf → sdf)、sdf_subtract(差(穴)、sdf × sdf → sdf)、sdf_intersect(積、sdf × sdf → sdf)、sdf_smooth_union(フィレットつきの和、sdf × sdf → sdf)、sdf_offset(肉厚のオフセット、sdf → sdf)、sdf_to_occupancy(SDF → 占有(スライスへ)、sdf → voxel)、grid_coords(評価格子、入力なし → coordgrid)、fit_superquadric(スーパー二次曲面の当てはめ(逆設計)、points → primitive)、fit_bspline_surface(自由曲面の当てはめ、image2d × image2d × image2d → bspline_surface)
• sim-to-real 較正(実物の画像から物理モデルの係数を合わせ、決まらない係数には数を出さない): simcal_model(順方向の物理モデルとつまみ(単位・範囲・カメラの雑音モデル)を組む、入力なし → simmodel)、simcal_simulate(つまみの値で模型を走らせて画像を作る、simmodel → images)、simcal_observe(実物の画像を位置合わせ・関心領域・露出の正規化をして模型に付ける、simmodel × images → simmodel)、simcal_screen(どのつまみが雑音を越えて画像を動かすか・交絡しているか、simmodel → table)、simcal_fit(重みつき最小二乗で合わせ、決まらないつまみは数を出さずに拒否する、simmodel → calresult)、simcal_identifiability(Cramér-Rao 下界とプロファイル尤度で、決まるつまみか・上限しか言えないかを判定する、calresult → calresult)、simcal_uncertainty(決まったつまみの区間(Laplace かブートストラップ)、calresult → calresult)、simcal_validate(取り置いた画像で過適合の門(LOCKED は 1 回だけ)、calresult × simmodel → calresult)、simcal_discrepancy(残差 PCA でモデルの食い違いを見つけ、不確かさが楽観側だと注記する、calresult → calresult)、simcal_report(較正の表 1 枚(走らせなかった検査は not run と書く)、calresult → table)
• 収束型の演算(多重反射・多重散乱・反復を収束まで回し、収束した証拠と残りの誤差を返す。収束しなければ値を出さない): iterate_fixed_point(相互反射や自己無撞着の式を不動点反復で解き(Anderson 加速)、収束しなければ拒否する、入力なし → table)、conv_observed_order(格子・反射回数・標本数を 3 段振った解から観測収束次数を出す、入力なし → table)、conv_gci(最も細かい解の離散化誤差を GCI で見積もる(シミュレーションの検証)、入力なし → table)、mc_sequential_mean(信頼区間が許容幅に入るまで標本を足し、使った標本数を返す、入力なし → table)、mc_photon_slab(濁った多層媒質の光子輸送(拡散反射・透過・吸収)を信頼区間つきで、入力なし → table)、iv_richardson_lucy_until(逆畳み込みを食い違い原理で止め、雑音の増幅を防ぐ、image2d → table)
• 波(レーダ・音・超音波): fmcw_range_profile(FMCW の距離、beatcube → signal)、beamform_delay_sum(ビームフォーミング、beatcube → signal)、range_doppler_map(距離・速度、beatcube → image2d)、angular_spectrum_propagate(波の伝搬、cimage → cimage)、stft(時間周波数、signal → table)、gcc_delay(到達時間差、signal × signal → measurement)、pulse_echo_ascan(超音波のパルスエコー(A スキャン)、table → table)、acoustic_layer_stack(積層の反射と透過(音響インピーダンス)、入力なし → table)、echo_phase_polarity(エコーの位相の反転でボイドを見分ける、table → table)、matched_filter_snr(既知の波形の整合フィルタ、signal × signal × pairs → signal)、music_doa(アンテナ・マイク列での到来方向(MUSIC、ビーム幅より細かく)、matrix → table)
• 文献のメタ議論(教育・レビュー) —— op なし(Fullseye の外)

sketch / user / interactive

This cluster covers interactive sketch-based interfaces and constraint-solving systems for creative and engineering design tasks.

• 使う op: box_sdf(直方体の SDF、coordgrid → sdf)、cylinder_sdf(円筒の SDF、coordgrid → sdf)、capsule_sdf(カプセル、coordgrid → sdf)、plane_sdf(平面(切り欠き)、coordgrid → sdf)、sdf_union(和、sdf × sdf → sdf)、sdf_subtract(差(穴)、sdf × sdf → sdf)、ほか 7 個: sdf_intersect、sdf_smooth_union、sdf_offset、sdf_to_occupancy、grid_coords、fit_superquadric、fit_bspline_surface
• 代表論文(新しい順 + いちばん古い 1 本):
  • Brian Hempel et al. (2019). Sketch-n-Sketch. 10.1145/3332165.3347925
  • Edgar Simo‐Serra et al. (2018). Real-time data-driven interactive rough sketch inking. 10.1145/3197517.3201370
  • Bridget M. Dixon et al. (2014). Geometric Sketch Constraint Solving with User Feedback. 10.2514/1.i010110
  • Robert Haimes et al. (2013). The Engineering Sketch Pad: A Solid-Modeling, Feature-Based, Web-Enabled System for Building Parametric Geometry. 10.2514/6.2013-3073
  • Bridget Dxon et al. (2013). Geometric Sketch Constraint Solving with User Feedback. 10.2514/6.2013-702

programming / cad / interface

This cluster covers the integration of programming paradigms with Computer-Aided Design (CAD) systems, focusing on parametric modeling, scripting interfaces, and programmatic control of design geometry.

• 使う op: box_sdf(直方体の SDF、coordgrid → sdf)、cylinder_sdf(円筒の SDF、coordgrid → sdf)、capsule_sdf(カプセル、coordgrid → sdf)、plane_sdf(平面(切り欠き)、coordgrid → sdf)、sdf_union(和、sdf × sdf → sdf)、sdf_subtract(差(穴)、sdf × sdf → sdf)、ほか 7 個: sdf_intersect、sdf_smooth_union、sdf_offset、sdf_to_occupancy、grid_coords、fit_superquadric、fit_bspline_surface
• 代表論文(新しい順 + いちばん古い 1 本):
  • J. González et al. (2023). Introducing Bidirectional Programming in Constructive Solid Geometry-Based CAD. 10.1145/3607822.3614521
  • Samuelle Bourgault et al. (2023). CoilCAM: Enabling Parametric Design for Clay 3D Printing Through an Action-Oriented Toolpath Programming System. 10.1145/3544548.3580745
  • Aman Mathur et al. (2020). Interactive Programming for Parametric CAD. 10.1111/cgf.14046
  • Felipe Machado et al. (2019). Parametric CAD modeling for open source scientific hardware: Comparing OpenSCAD and FreeCAD Python scripts. 10.1371/journal.pone.0225795
  • Annika Geis et al. (2015). Use of Vectorial Tolerances for Direct Representation and Analysis in CAD-systems. 10.1016/j.procir.2015.04.071
  • Robert Haimes et al. (2012). On The Construction of Aircraft Conceptual Geometry for High-Fidelity Analysis and Design. 10.2514/6.2012-683

cad / parametric / modeling

This cluster covers parametric and feature-based CAD modeling methodologies, focusing on how complex engineering designs are created, modified, and reused through parametric systems.

• 使う op: box_sdf(直方体の SDF、coordgrid → sdf)、cylinder_sdf(円筒の SDF、coordgrid → sdf)、capsule_sdf(カプセル、coordgrid → sdf)、plane_sdf(平面(切り欠き)、coordgrid → sdf)、sdf_union(和、sdf × sdf → sdf)、sdf_subtract(差(穴)、sdf × sdf → sdf)、ほか 17 個: sdf_intersect、sdf_smooth_union、sdf_offset、sdf_to_occupancy、grid_coords、fit_superquadric、fit_bspline_surface、simcal_model、simcal_simulate、simcal_observe、simcal_screen、simcal_fit、simcal_identifiability、simcal_uncertainty、simcal_validate、simcal_discrepancy、simcal_report
• 代表論文(新しい順 + いちばん古い 1 本):
  • Seda Yilmaz et al. (2024). CAD and constructive solid geometry modeling of the Molten Salt Reactor Experiment with OpenMC. 10.3389/fnuen.2024.1385478
  • Mutahar Safdar et al. (2020). Feature-based translation of CAD models with macro-parametric approach: issues of feature mapping, persistent naming, and constraint translation. 10.1093/jcde/qwaa043
  • Nedim Pervan et al. (2019). Development of Parametric CAD Model and Structural Analysis of the Car Jack. 10.12913/22998624/109791
  • E. Jayakiran Reddy et al. (2018). Development of knowledge based parametric CAD modeling system for spur gear: An approach. 10.1016/j.aej.2018.07.010
  • Andreas Grandicki et al. (2017). Parametric CAD Modeling to aid Simulation-Driven Design : An evaluation and improvement of methods used at Scania. W2623265238
  • Yongming Wang et al. (2012). UML Modeling and Parametric Design for Cross Shaft Universal Coupling CAD System. 10.4304/jsw.7.9.2069-2075

education / students / engineering

This cluster covers pedagogical approaches and technical methodologies for teaching advanced engineering design and CAD/CAE tools to students and preservice educators.

• 代表論文(新しい順 + いちばん古い 1 本):
  • Myela Paige et al. (2023). The Impact of Hands-on Geometric Dimensioning and Tolerancing Intervention Activities on Students in Engineering Design. 10.18260/3-1-1153-36044
  • Loris Barbieri et al. (2022). Performance-Driven Engineering Design Approaches Based on Generative Design and Topology Optimization Tools: A Comparative Study. 10.3390/app12042106
  • Federico Campi et al. (2021). CAD-integrated design for manufacturing and assembly in mechanical design. 10.1080/0951192x.2021.1992659
  • Kuen‐Yi Lin et al. (2021). Effects of infusing the engineering design process into STEM project-based learning to develop preservice technology teachers’ engineering design thinking. 10.1186/s40594-020-00258-9
  • German HERRERA-GRANADOS et al. (2021). Recording of machine tool operation plan for the generation of the CNC programs of a parametric CAD model. 10.1299/jsmelem.2021.10.185-177
  • Daniel L. Householder et al. (2012). Incorporating Engineering Design Challenges into STEM Courses. W2162934093

cad / journal / publishes

This cluster encompasses Computer-Aided Design (CAD) and Applications journal publications covering methodologies for design modeling, reverse engineering, parametric assembly, and generative design approaches.

• 使う op: box_sdf(直方体の SDF、coordgrid → sdf)、cylinder_sdf(円筒の SDF、coordgrid → sdf)、capsule_sdf(カプセル、coordgrid → sdf)、plane_sdf(平面(切り欠き)、coordgrid → sdf)、sdf_union(和、sdf × sdf → sdf)、sdf_subtract(差(穴)、sdf × sdf → sdf)、ほか 7 個: sdf_intersect、sdf_smooth_union、sdf_offset、sdf_to_occupancy、grid_coords、fit_superquadric、fit_bspline_surface
• 代表論文(新しい順 + いちばん古い 1 本):
  • Tim Brix Nerenst et al. (2022). Parametric CAD Modeling: New Principles for Robust Sketch Constraints. 10.14733/cadaps.2023.56-81
  • Harald E. Otto et al. (2020). Parametric Feature-Based Solid Model Deficiency Identification to Support Learning Outcomes Assessment in CAD Education. 10.14733/cadaps.2021.411-442
  • Francesco Buonamici et al. (2020). Generative Design: An Explorative Study. 10.14733/cadaps.2021.144-155
  • Jérémy Montlahuc et al. (2019). As-scanned Point Clouds Generation for Virtual Reverse Engineer-ing of CAD Assembly Models. 10.14733/cadaps.2019.1171-1182
  • Jeffrey Otey et al. (2018). Assessment of Parametric Assembly Models Based on CAD Quality Dimensions. 10.14733/cadaps.2019.628-653
  • Michele Bici et al. (2014). Study of a Point Cloud Segmentation with Part Type Recognition for Tolerance Inspection of Plastic Components via Reverse Engineering. 10.1080/16864360.2014.914382

cad / parametric / design

This cluster covers the intersection of Computer-Aided Design (CAD) systems, parametric modeling approaches, and their enhancement through artificial intelligence and machine learning technologies.

• 使う op: box_sdf(直方体の SDF、coordgrid → sdf)、cylinder_sdf(円筒の SDF、coordgrid → sdf)、capsule_sdf(カプセル、coordgrid → sdf)、plane_sdf(平面(切り欠き)、coordgrid → sdf)、sdf_union(和、sdf × sdf → sdf)、sdf_subtract(差(穴)、sdf × sdf → sdf)、ほか 7 個: sdf_intersect、sdf_smooth_union、sdf_offset、sdf_to_occupancy、grid_coords、fit_superquadric、fit_bspline_surface
• 代表論文(新しい順 + いちばん古い 1 本):
  • Qianzhi Jing et al. (2026). Req2CAD: bridging functional requirements and parametric CAD models to support conceptual 3D design. 10.1145/3772318.3791949
  • Jiahao Li et al. (2026). ReCAD: Reinforcement Learning Enhanced Parametric CAD Model Generation with Vision-Language Models. 10.1609/aaai.v40i8.37544
  • Aman Kukreja et al. (2026). From imprecision to precision in virtual reality: a machine learning approach to parametric CAD modelling. 10.1080/09544828.2026.2616581
  • JianFei Liang et al. (2025). CADCL: Reconstruct parametric CAD models from B-rep via contrastive learning. 10.1093/jcde/qwaf102
  • Nirmal Panta et al. (2025). MEDA: A Multi-Agent System For Parametric CAD Model Creation. 10.1115/detc2025-163946
  • M. A. R. Paramio et al. (2012). Assessing red deer antler density with a hydrostatic method versus a new parametric volume-modelling technique using 3D-CAD. 10.1071/an12015

scattering / sar / radar

This cluster covers techniques for automatically extracting and modeling scattering centers from complex radar targets to enable synthetic aperture radar (SAR) automatic target recognition (ATR).

• 使う op: iterate_fixed_point(相互反射や自己無撞着の式を不動点反復で解き(Anderson 加速)、収束しなければ拒否する、入力なし → table)、conv_observed_order(格子・反射回数・標本数を 3 段振った解から観測収束次数を出す、入力なし → table)、conv_gci(最も細かい解の離散化誤差を GCI で見積もる(シミュレーションの検証)、入力なし → table)、mc_sequential_mean(信頼区間が許容幅に入るまで標本を足し、使った標本数を返す、入力なし → table)、mc_photon_slab(濁った多層媒質の光子輸送(拡散反射・透過・吸収)を信頼区間つきで、入力なし → table)、iv_richardson_lucy_until(逆畳み込みを食い違い原理で止め、雑音の増幅を防ぐ、image2d → table)、ほか 11 個: fmcw_range_profile、beamform_delay_sum、range_doppler_map、angular_spectrum_propagate、stft、gcc_delay、pulse_echo_ascan、acoustic_layer_stack、echo_phase_polarity、matched_filter_snr、music_doa
• 代表論文(新しい順 + いちばん古い 1 本):
  • Jin Liu et al. (2020). An Automatic and Forward Method to Establish 3-D Parametric Scattering Center Models of Complex Targets for Target Recognition. 10.1109/tgrs.2020.2989856
  • Yang He et al. (2014). A Forward Approach to Establish Parametric Scattering Center Models for Known Complex Radar Targets Applied to SAR ATR. 10.1109/tap.2014.2360700

students / education / courses

This cluster covers pedagogical approaches and curriculum design strategies for engineering education, with emphasis on developing practical problem-solving and design skills.

• 代表論文(新しい順 + いちばん古い 1 本):
  • Lucy King et al. (2020). A Structure For Integration Of Manufacturing And Mechanical Design Engineering Courses. 10.18260/1-2–11748
  • Ramesh Kuppuswamy et al. (2020). Project-based learning in an engineering-design course – developing mechanical- engineering graduates for the world of work. 10.1016/j.procir.2020.02.215
  • Lyn D. English et al. (2016). Advancing integrated STEM learning through engineering design: Sixth-grade students’ design and construction of earthquake resistant buildings. 10.1080/00220671.2016.1264053
  • Reuven Katz (2015). Integrating Analysis and Design in Mechanical Engineering Education. 10.1016/j.procir.2015.01.042
  • Shanna Daly et al. (2014). Teaching Creativity in Engineering Courses. 10.1002/jee.20048
  • E.M. Lavrischeva et al. (2012). Approach to E-Learning Fundamental Aspects of Software Engineering. 10.55056/ceur-ws.org/vol-848/icteri-2012-ceur-ws-paper-17-p-176-187.pdf

fatigue / bolted / joints

206 本

This cluster addresses the mechanical behavior, fatigue performance, and structural integrity of bolted joints and fastened assemblies. It encompasses analysis methods (finite element, system dynamics), failure mechanisms (looseness, fatigue), and detection techniques for ensuring reliability in critical infrastructure, aerospace, automotive, and biomedical applications.

• Bolt looseness detection can be automated using deep learning and acoustic/ultrasonic monitoring methods to prevent catastrophic failure in infrastructure
• Fatigue resistance in bolted connections is influenced by preload, vibration, cyclic loading, and material properties; high-speed dynamic environments require enhanced assessment approaches
• Finite element analysis (FEA) provides a unified computational framework for analyzing complex structures with bolted joints under various loading conditions
• Joint stiffness and gasket behavior significantly affect load distribution, stress concentration, and fatigue life in bolted assemblies
• Material properties (polymers, composites, ceramics, hard carbons) interact with joint design to determine overall strength, toughness, and fatigue performance

使う op(テーマ → op の対応表から。ノートで型と例を確かめてから使う):

• き裂・ひずみ・損傷の画像計測: strain_from_displacement(全視野ひずみ、image2d × image2d → image2d)、piv_multipass(多段の変位推定、image2d × image2d → flow2d)、motion_magnify(微小変形の拡大、video → table)、displacement_series(変位の時系列、video → pairs)、vol_frangi(CT 中の管状き裂の強調、voxel → voxel)、canny(き裂のエッジ、image → region)、hessian3d(3D の線状構造、voxel → hessian)、distance_ridge(き裂の中心線、voxel → voxel)
• 収束型の演算(多重反射・多重散乱・反復を収束まで回し、収束した証拠と残りの誤差を返す。収束しなければ値を出さない): iterate_fixed_point(相互反射や自己無撞着の式を不動点反復で解き(Anderson 加速)、収束しなければ拒否する、入力なし → table)、conv_observed_order(格子・反射回数・標本数を 3 段振った解から観測収束次数を出す、入力なし → table)、conv_gci(最も細かい解の離散化誤差を GCI で見積もる(シミュレーションの検証)、入力なし → table)、mc_sequential_mean(信頼区間が許容幅に入るまで標本を足し、使った標本数を返す、入力なし → table)、mc_photon_slab(濁った多層媒質の光子輸送(拡散反射・透過・吸収)を信頼区間つきで、入力なし → table)、iv_richardson_lucy_until(逆畳み込みを食い違い原理で止め、雑音の増幅を防ぐ、image2d → table)
• 機械要素(歯車・ねじ・機構): polar_unwrap(歯車の極座標展開(歯形)、image2d → image2d)、cylinder_unwrap(円筒面の展開(ねじ山)、voxel → voxel)、m1_measure_pairs(歯厚・ピッチ、image → feature)、edges_sub_pix(歯形の輪郭、image → contour)、envelope_spectrum(歯車の噛み合い振動、signal → table)、order_spectrum(次数スペクトル、signal → table)、segment_rigid_motions(機構の動く部品の分離、points × points → labels)

rock / bolts / bolt

This cluster covers the design, analysis, and performance of bolted connections in structural and geotechnical applications, with emphasis on rock bolts and cable bolts used in mining, tunneling, and underground support systems.

• 使う op: polar_unwrap(歯車の極座標展開(歯形)、image2d → image2d)、cylinder_unwrap(円筒面の展開(ねじ山)、voxel → voxel)、m1_measure_pairs(歯厚・ピッチ、image → feature)、edges_sub_pix(歯形の輪郭、image → contour)、envelope_spectrum(歯車の噛み合い振動、signal → table)、order_spectrum(次数スペクトル、signal → table)、ほか 2 個: segment_rigid_motions、displacement_series
• 代表論文(新しい順 + いちばん古い 1 本):
  • Jingsheng Zhou et al. (2025). Ductile Fracture Simulation of Shear-Out Strength Behavior and Design Equations of Bolted Lap Joints. 10.1061/jsendh.steng-14623
  • Jianbin Wu et al. (2023). Design theory and numerical analysis of earthquake‐resilient joint with slotted bolted connection. 10.1002/tal.2053
  • Manchao He et al. (2022). Experimental study on the shear performance of quasi-NPR steel bolted rock joints. 10.1016/j.jrmge.2022.03.011
  • Marcin Szpunar et al. (2021). Central Composite Design Optimisation in Single Point Incremental Forming of Truncated Cones from Commercially Pure Titanium Grade 2 Sheet Metals. 10.3390/ma14133634
  • Emad Jahangir et al. (2021). Development and application of an interface constitutive model for fully grouted rock-bolts and cable-bolts. 10.1016/j.jrmge.2021.03.011
  • A. V. Khomenko et al. (2012). Statistical theory of the boundary friction of atomically flat solid surfaces in the presence of a lubricant layer. 10.3367/ufne.0182.201210f.1081

fatigue / strength / fracture

This cluster covers the mechanics of material failure under cyclic loading, fatigue resistance, and structural integrity assessment.

• 使う op: strain_from_displacement(全視野ひずみ、image2d × image2d → image2d)、piv_multipass(多段の変位推定、image2d × image2d → flow2d)、motion_magnify(微小変形の拡大、video → table)、displacement_series(変位の時系列、video → pairs)、vol_frangi(CT 中の管状き裂の強調、voxel → voxel)、canny(き裂のエッジ、image → region)、ほか 2 個: hessian3d、distance_ridge
• 代表論文(新しい順 + いちばん古い 1 本):
  • A. F. Hobbacher et al. (2024). Recommendations for Fatigue Design of Welded Joints and Components. 10.1007/978-3-031-57667-6
  • Oleg Gaidai et al. (2023). Floating wind turbines structural details fatigue life assessment. 10.1038/s41598-023-43554-4
  • Feng Guo et al. (2022). System dynamics in structural strength and vibration fatigue life assessment of the swing bar for high‐speed maglev train. 10.1002/msd2.12045
  • Luca Susmel (2022). Notches, nominal stresses, fatigue strength reduction factors and constant/variable amplitude multiaxial fatigue loading. 10.1016/j.ijfatigue.2022.106941
  • Ansel C. Ugural (2022). Mechanical Engineering Design. 10.1201/9781003251378
  • M. Ramji et al. (2012). Design of composite patch reinforcement applied to mixed-mode cracked panel using finite element analysis. 10.1177/0731684412440601

bolted / joints / bolt

This cluster covers the mechanical behavior, design, and analysis of bolted and jointed structures, with primary focus on composite materials, hybrid joining methods, and interference fit connections.

• 使う op: strain_from_displacement(全視野ひずみ、image2d × image2d → image2d)、piv_multipass(多段の変位推定、image2d × image2d → flow2d)、motion_magnify(微小変形の拡大、video → table)、displacement_series(変位の時系列、video → pairs)、vol_frangi(CT 中の管状き裂の強調、voxel → voxel)、canny(き裂のエッジ、image → region)、ほか 9 個: hessian3d、distance_ridge、polar_unwrap、cylinder_unwrap、m1_measure_pairs、edges_sub_pix、envelope_spectrum、order_spectrum、segment_rigid_motions
• 代表論文(新しい順 + いちばん古い 1 本):
  • Yu Chen et al. (2024). Load transfer mechanism and design strength of hybrid bolted joint with friction- and bearing-type connections. 10.1016/j.istruc.2024.108020
  • Omar A.I. Azeem (2024). A machine learning assisted preliminary design methodology for bolted composite joints in large structures. 10.1017/aer.2024.104
  • Kadir Sarı et al. (2024). A Software for Optimum Design of Laterally Loaded Bolted Joints. 10.29109/gujsc.1385010
  • Emre Erbil et al. (2023). Fastening solutions on composite structures: Progressive damage analysis of a unique bolted joint design for single-lap CFRP laminate. 10.1080/15376494.2023.2272240
  • Dario Croccolo et al. (2023). Optimization of Bolted Joints: A Literature Review. 10.3390/met13101708
  • Albin K.J. Hasselström et al. (2012). Thermal Contact Conductance in Bolted Joints. W168835186

fiber / strength / reinforced

This cluster covers the engineering and material science of fiber-reinforced and composite structures designed to achieve high strength and durability under demanding conditions.

• 使う op: strain_from_displacement(全視野ひずみ、image2d × image2d → image2d)、piv_multipass(多段の変位推定、image2d × image2d → flow2d)、motion_magnify(微小変形の拡大、video → table)、displacement_series(変位の時系列、video → pairs)、vol_frangi(CT 中の管状き裂の強調、voxel → voxel)、canny(き裂のエッジ、image → region)、ほか 2 個: hessian3d、distance_ridge
• 代表論文(新しい順 + いちばん古い 1 本):
  • Asha Viswanath et al. (2022). Design of novel isosurface strut-based lattice structures: Effective stiffness, strength, anisotropy and fatigue properties. 10.1016/j.matdes.2022.111293
  • Kenneth Kanayo Alaneme et al. (2022). Application of finite element analysis for optimizing selection and design of Ti-based biometallic alloys for fractures and tissues rehabilitation: a review. 10.1016/j.jmrt.2022.05.001
  • Peiyuan Zuo et al. (2021). Review of hybrid composites fatigue. 10.1016/j.compstruct.2021.114358
  • Chung‐Chan Hung et al. (2021). A Review of Developments and Challenges for UHPC in Structural Engineering: Behavior, Analysis, and Design. 10.1061/(asce)st.1943-541x.0003073
  • Haiyuan Zhao et al. (2021). Design and Mechanical Properties Verification of Gradient Voronoi Scaffold for Bone Tissue Engineering. 10.3390/mi12060664
  • Qiang Fu et al. (2013). Toward Strong and Tough Glass and Ceramic Scaffolds for Bone Repair. 10.1002/adfm.201301121

finite / element / analysis

This cluster covers the application of finite element analysis (FEA) as a computational method for modeling, simulating, and optimizing the structural behavior of complex mechanical systems and components.

• 使う op: strain_from_displacement(全視野ひずみ、image2d × image2d → image2d)、piv_multipass(多段の変位推定、image2d × image2d → flow2d)、motion_magnify(微小変形の拡大、video → table)、displacement_series(変位の時系列、video → pairs)、vol_frangi(CT 中の管状き裂の強調、voxel → voxel)、canny(き裂のエッジ、image → region)、ほか 8 個: hessian3d、distance_ridge、iterate_fixed_point、conv_observed_order、conv_gci、mc_sequential_mean、mc_photon_slab、iv_richardson_lucy_until
• 代表論文(新しい順 + いちばん古い 1 本):
  • Mohamad Norfaieqwan Bin Kamarudin et al. (2022). Buckling Analysis of a Thin-Walled Structure Using Finite Element Method and Design of Experiments. 10.3390/aerospace9100541
  • Alberto Ciampaglia et al. (2020). Design and analysis of automotive lightweight materials suspension based on finite element analysis. 10.1177/0954406220947457
  • Ivan Argatov et al. (2020). Contact Geometry Adaptation in Fretting Wear: A Constructive Review. 10.3389/fmech.2020.00051
  • Dejun Jia et al. (2019). Design and simulation analysis of Lattice bone plate based on finite element method. 10.1080/15376494.2019.1665759
  • Thanh Q. Nguyen (2019). Finite Element Analysis in Automobile Chassis Design. 10.4028/www.scientific.net/amm.889.461
  • Bandarupalli Praneeth (2012). Finite Element Analysis of Pressure Vessel and Piping Design. W2187180293

bolted / joint / gasket

This cluster covers the engineering analysis and design of bolted joints, gaskets, and related structural connections in mechanical systems.

• 使う op: strain_from_displacement(全視野ひずみ、image2d × image2d → image2d)、piv_multipass(多段の変位推定、image2d × image2d → flow2d)、motion_magnify(微小変形の拡大、video → table)、displacement_series(変位の時系列、video → pairs)、vol_frangi(CT 中の管状き裂の強調、voxel → voxel)、canny(き裂のエッジ、image → region)、ほか 2 個: hessian3d、distance_ridge
• 代表論文(新しい順 + いちばん古い 1 本):
  • Ines Boujnah et al. (2025). Towards precision in bolted joint design: a preliminary machine learning-based parameter prediction. 10.1017/pds.2025.10335
  • Changjun Zhong et al. (2025). Experimental, numerical simulation and design methodology for a novel sleeve fully bolted joint in modular buildings. 10.1016/j.jcsr.2025.109763
  • Yihao Dong et al. (2025). Structural topology optimization of aircraft wing leading edge fabricated of multilayer composites. 10.1016/j.ast.2025.109993
  • Péter Grubits et al. (2024). Structural topology optimization for plastic-limit behavior of I-beams, considering various beam-column connections. 10.1080/15397734.2024.2412757
  • Nidhal Jamia et al. (2020). An equivalent model of a nonlinear bolted flange joint. 10.1016/j.ymssp.2020.107507
  • E. V. Zaretsky (2012). Rolling bearing steels – a technical and historical perspective. 10.1179/1743284711y.0000000043

bolt / preload / bolted

This cluster addresses detection, measurement, and monitoring of bolt preload and looseness in structural assemblies using various sensing and analysis techniques.

• 使う op: strain_from_displacement(全視野ひずみ、image2d × image2d → image2d)、piv_multipass(多段の変位推定、image2d × image2d → flow2d)、motion_magnify(微小変形の拡大、video → table)、displacement_series(変位の時系列、video → pairs)、vol_frangi(CT 中の管状き裂の強調、voxel → voxel)、canny(き裂のエッジ、image → region)、ほか 9 個: hessian3d、distance_ridge、polar_unwrap、cylinder_unwrap、m1_measure_pairs、edges_sub_pix、envelope_spectrum、order_spectrum、segment_rigid_motions
• 代表論文(新しい順 + いちばん古い 1 本):
  • Gregory McKiernan et al. (2026). Design and Measurements of the BOLT-1B Joint Steps. 10.2514/1.a36741
  • Cheng Yuan et al. (2021). Automated structural bolt looseness detection using deep learning‐based prediction model. 10.1002/stc.2899
  • Rusong Miao et al. (2020). A Review of Bolt Tightening Force Measurement and Loosening Detection. 10.3390/s20113165
  • Aaron Rimpel et al. (2019). Simple Contact Stiffness Model Validation for Tie Bolt Rotor Design With Butt Joints and Pilot Fits. 10.1115/1.4045102
  • Qingchao Sun et al. (2019). Bolt preload measurement based on the acoustoelastic effect using smart piezoelectric bolt. 10.1088/1361-665x/ab06dc
  • Igor Pavelko et al. (2014). Bolt-joint structural health monitoring by the method of electromechanical impedance. 10.1108/aeat-01-2013-0006

implant / bone / stress

This cluster addresses biomechanical analysis of implants (orthopedic and dental) and stress distribution in bone-implant systems.

• 使う op: strain_from_displacement(全視野ひずみ、image2d × image2d → image2d)、piv_multipass(多段の変位推定、image2d × image2d → flow2d)、motion_magnify(微小変形の拡大、video → table)、displacement_series(変位の時系列、video → pairs)、vol_frangi(CT 中の管状き裂の強調、voxel → voxel)、canny(き裂のエッジ、image → region)、ほか 2 個: hessian3d、distance_ridge
• 代表論文(新しい順 + いちばん古い 1 本):
  • René Burchard et al. (2023). Stress shielding effect after total hip arthroplasty varies between combinations of stem design and stiffness—a comparing biomechanical finite element analysis. 10.1007/s00264-023-05825-7
  • Vamsi Krishna Dommeti et al. (2023). Design of customized coated dental implants using finite element analysis. 10.17219/dmp/142447
  • Ziting Zheng et al. (2022). Influence of margin design and restorative material on the stress distribution of endocrowns: a 3D finite element analysis. 10.1186/s12903-022-02063-y
  • Yash Vardhana Gupta et al. (2020). Design of dental implant using design of experiment and topology optimization: A finite element analysis study. 10.1177/0954411920967146
  • Rawa Omar Ibrahim et al. (2020). Mechanical and thermal stress evaluation of PEEK prefabricated post with different head design in endodontically treated tooth: 3D-finite element analysis. 10.4012/dmj.2020-053
  • SR Desai et al. (2012). Evaluation of design parameters of eight dental implant designs: A two-dimensional finite element analysis. 10.4103/1119-3077.97308

optimization / topology / design

256 本

This cluster covers optimization methodologies applied to topology and design problems, ranging from structural engineering and metamaterial design to chemical processes and reservoir management. It encompasses techniques like topology optimization, surrogate modeling, genetic algorithms, and level set methods used to discover optimal configurations while satisfying manufacturing, performance, and computational constraints.

• Topology Optimization Methods: Level set functions, density-based approaches, and genetic algorithms enable automated discovery of optimal structural layouts and hole insertion strategies
• Inverse Design Frameworks: Machine learning and backpropagation-based approaches allow target-driven material and structure discovery without exhaustive design space exploration
• Surrogate Modeling: Reduced-order models and neural networks approximate expensive simulations, enabling efficient multi-objective optimization in complex domains
• Metamaterial Design: Topology optimization produces subwavelength bandgaps and energy-absorbing structures with novel acoustic and mechanical properties
• Manufacturing Constraints: Moldability conditions, casting feasibility, and fabrication limitations must be integrated as explicit constraints in optimization formulations

使う op(テーマ → op の対応表から。ノートで型と例を確かめてから使う):

• トポロジー最適化・格子構造の評価 —— 入口: 進化エンジン(docs/ENGINE.md、docs/EVOLUTION_ENVIRONMENT.md)は設計パラメータを適応度で回す側
• トポロジー最適化・格子構造の評価: vol_euler_number(連結性(閉じた空隙の数)、voxel → measurement)、vol_wall_thickness(最小肉厚、voxel → signal)、edt_jfa(距離変換(肉厚・隙間)、voxel → sdf)、vol_granulometry(空隙の大きさ分布、voxel → measurement)、mesh_volume(体積(材料量)、mesh → measurement)、inertia_tensor(慣性テンソル、points → matrix)、principal_moments(主慣性モーメント、points → descriptor)
• 実験計画法・応答曲面・品質工学・感度解析(工程条件の決め方): doe_orthogonal_array(直交表(L4〜L27、公表された表)で条件を少ない回数で振る、入力なし → design)、doe_fractional_factorial(一部実施要因計画(分解能と交絡表つき)、入力なし → design)、doe_central_composite(応答曲面のための中心複合計画、入力なし → design)、doe_box_behnken(角を踏まない応答曲面の計画、入力なし → design)、doe_lhs(シミュレーションの入力をラテン超方格で埋める、入力なし → design)、doe_encode(条件の記録を符号化(−1..+1)へ、matrix → design)、doe_decode(計画を装置の設定値へ戻す、design → matrix)、doe_main_effects(主効果の応答表(SN 比は参考、判断は平均と散らばり)、design × signal → table)、rsm_fit(応答曲面・分散分析・停留点(支えない次数は拒否)、design × signal → table)、mv_pls(条件 ⇔ 画像の特徴量の部分最小二乗(Q² と VIP)、matrix × signal → table)、mv_pca(特徴量の主成分と独立な次元の数、matrix → table)、feat_image_vector(画像を名前と単位つきの特徴量の表に、images → table)、doe_morris(効くつまみを少ない回数でふるう(Morris)、入力なし → design)、sens_morris(要素効果の μ* と σ、design × signal → table)、doe_saltelli(Sobol’ 指数のための標本行列、入力なし → design)、sens_sobol(Sobol’ の S1・ST(交互作用の分も)と信頼区間、design × signal → table)、mv_confounding(データから決まるパラメータの組(共線性指数)、matrix → table)、mv_residual_pca(残差に残った構造 = モデルに足りないもの、matrix → table)
• アクチュエータ(制御は外、動きを画像で測る側): displacement_series(変位の時系列、video → pairs)、phase_displacement(位相ベースの微小変位、video → table)、riesz_displacement_series(Riesz 変換の変位、video → pairs)、temporal_band_power(帯域ごとの動きの量、video → image2d)、band_snr(動きの SN、video → table)、order_spectrum(回転次数、signal → table)、angular_resample(回転同期、signal → table)、piv_multipass(流体(油空圧)の流れ場、image2d × image2d → flow2d)
• 機械要素(歯車・ねじ・機構): polar_unwrap(歯車の極座標展開(歯形)、image2d → image2d)、cylinder_unwrap(円筒面の展開(ねじ山)、voxel → voxel)、m1_measure_pairs(歯厚・ピッチ、image → feature)、edges_sub_pix(歯形の輪郭、image → contour)、envelope_spectrum(歯車の噛み合い振動、signal → table)、segment_rigid_motions(機構の動く部品の分離、points × points → labels)

gear / optimization / gears

This cluster focuses on computational optimization techniques for gear design, covering methods to simultaneously minimize weight, noise, and transmission error while maintaining structural integrity and efficiency.

• 使う op: vol_euler_number(連結性(閉じた空隙の数)、voxel → measurement)、vol_wall_thickness(最小肉厚、voxel → signal)、edt_jfa(距離変換(肉厚・隙間)、voxel → sdf)、vol_granulometry(空隙の大きさ分布、voxel → measurement)、mesh_volume(体積(材料量)、mesh → measurement)、inertia_tensor(慣性テンソル、points → matrix)、ほか 27 個: principal_moments、doe_orthogonal_array、doe_fractional_factorial、doe_central_composite、doe_box_behnken、doe_lhs、doe_encode、doe_decode、doe_main_effects、rsm_fit、mv_pls、mv_pca、feat_image_vector、doe_morris、sens_morris、doe_saltelli、sens_sobol、mv_confounding、mv_residual_pca、polar_unwrap、cylinder_unwrap、m1_measure_pairs、edges_sub_pix、envelope_spectrum、order_spectrum、segment_rigid_motions、displacement_series
• 代表論文(新しい順 + いちばん古い 1 本):
  • Yihong Gu et al. (2025). Study on the Dynamic Characteristics of the Gear Lubrication Flow Field with Baffles and Optimization Design Strategies. 10.3390/lubricants13040143
  • Marah A. Elsiedy et al. (2024). Multi-objective design optimization of polymer spur gears using a hybrid approach. 10.1186/s44147-024-00443-5
  • Yunda Zhao et al. (2024). Multi-Objective Optimization Design of Cycloid-Pin Gears Based on RV Reducer Precision Transmission Performance. 10.3390/en17030654
  • Chiu-Hung Chen (2023). Application of Multiple Deep Neural Networks to Multi-Solution Synthesis of Linkage Mechanisms. 10.3390/machines11111018
  • Wen Xin et al. (2023). A multi-objective optimization design approach of large mining planetary gear reducer. 10.1038/s41598-023-45745-5
  • S. Padmanabhan et al. (2012). A Performance Study of Real Coded Genetic Algorithm on Gear Design Optimization. 10.4028/www.scientific.net/amr.622-623.64

topology / optimization / structural

This cluster covers structural design optimization techniques that integrate topology optimization with machine learning, numerical methods, and manufacturing constraints.

• 使う op: vol_euler_number(連結性(閉じた空隙の数)、voxel → measurement)、vol_wall_thickness(最小肉厚、voxel → signal)、edt_jfa(距離変換(肉厚・隙間)、voxel → sdf)、vol_granulometry(空隙の大きさ分布、voxel → measurement)、mesh_volume(体積(材料量)、mesh → measurement)、inertia_tensor(慣性テンソル、points → matrix)、ほか 19 個: principal_moments、doe_orthogonal_array、doe_fractional_factorial、doe_central_composite、doe_box_behnken、doe_lhs、doe_encode、doe_decode、doe_main_effects、rsm_fit、mv_pls、mv_pca、feat_image_vector、doe_morris、sens_morris、doe_saltelli、sens_sobol、mv_confounding、mv_residual_pca
• 代表論文(新しい順 + いちばん古い 1 本):
  • Weiwei He et al. (2024). Multi-level physics informed deep learning for solving partial differential equations in computational structural mechanics. 10.1038/s44172-024-00303-3
  • Yang Liu et al. (2024). Ultrastiff metamaterials generated through a multilayer strategy and topology optimization. 10.1038/s41467-024-47089-8
  • Martin Juan José Bucher et al. (2023). Performance-Based Generative Design for Parametric Modeling of Engineering Structures Using Deep Conditional Generative Models. 10.1016/j.autcon.2023.105128
  • Hyogu Jeong et al. (2023). A complete Physics-Informed Neural Network-based framework for structural topology optimization. 10.1016/j.cma.2023.116401
  • Tobias Würth et al. (2023). Physics-informed neural networks for data-free surrogate modelling and engineering optimization – An example from composite manufacturing. 10.1016/j.matdes.2023.112034
  • Peter D. Dunning et al. (2012). A new hole insertion method for level set based structural topology optimization. 10.1002/nme.4384

surrogate / optimization / fidelity

This cluster covers surrogate modeling and multi-fidelity optimization techniques used to reduce computational costs in complex engineering design and simulation problems.

• 使う op: vol_euler_number(連結性(閉じた空隙の数)、voxel → measurement)、vol_wall_thickness(最小肉厚、voxel → signal)、edt_jfa(距離変換(肉厚・隙間)、voxel → sdf)、vol_granulometry(空隙の大きさ分布、voxel → measurement)、mesh_volume(体積(材料量)、mesh → measurement)、inertia_tensor(慣性テンソル、points → matrix)、ほか 19 個: principal_moments、doe_orthogonal_array、doe_fractional_factorial、doe_central_composite、doe_box_behnken、doe_lhs、doe_encode、doe_decode、doe_main_effects、rsm_fit、mv_pls、mv_pca、feat_image_vector、doe_morris、sens_morris、doe_saltelli、sens_sobol、mv_confounding、mv_residual_pca
• 代表論文(新しい順 + いちばん古い 1 本):
  • Sergiy Plankovskyy et al. (2025). Review of Physics-Informed Neural Networks: Challenges in Loss Function Design and Geometric Integration. 10.3390/math13203289
  • Musaddiq Al Ali et al. (2024). Metaheuristic aided structural topology optimization method for heat sink design with low electromagnetic interference. 10.1038/s41598-024-54083-z
  • David J. J. Toal (2023). Applications of multi-fidelity multi-output Kriging to engineering design optimization. 10.1007/s00158-023-03567-z
  • Maolin Shi et al. (2023). A multi-fidelity surrogate model based on extreme support vector regression: fusing different fidelity data for engineering design. 10.1108/ec-10-2021-0583
  • Kai Wang et al. (2023). An efficient geometric constraint handling method for surrogate-based aerodynamic shape optimization. 10.1080/19942060.2022.2153173
  • Sławomir Kozieł et al. (2012). Simulation-Driven Design Optimization and Modeling for Microwave Engineering. 10.1142/p860

bayesian / optimization / algorithms

This cluster covers Bayesian optimization and advanced algorithmic approaches for efficiently exploring complex parameter spaces and objective functions.

• 使う op: vol_euler_number(連結性(閉じた空隙の数)、voxel → measurement)、vol_wall_thickness(最小肉厚、voxel → signal)、edt_jfa(距離変換(肉厚・隙間)、voxel → sdf)、vol_granulometry(空隙の大きさ分布、voxel → measurement)、mesh_volume(体積(材料量)、mesh → measurement)、inertia_tensor(慣性テンソル、points → matrix)、ほか 19 個: principal_moments、doe_orthogonal_array、doe_fractional_factorial、doe_central_composite、doe_box_behnken、doe_lhs、doe_encode、doe_decode、doe_main_effects、rsm_fit、mv_pls、mv_pca、feat_image_vector、doe_morris、sens_morris、doe_saltelli、sens_sobol、mv_confounding、mv_residual_pca
• 代表論文(新しい順 + いちばん古い 1 本):
  • Bernd Bischl et al. (2023). Hyperparameter optimization: Foundations, algorithms, best practices, and open challenges. 10.1002/widm.1484
  • Bowen Lei et al. (2021). Bayesian optimization with adaptive surrogate models for automated experimental design. 10.1038/s41524-021-00662-x
  • José E. Rayas‐Sánchez et al. (2021). Advanced RF and Microwave Design Optimization: A Journey and a Vision of Future Trends. 10.1109/jmw.2020.3034263
  • Yichi Zhang et al. (2020). Bayesian Optimization for Materials Design with Mixed Quantitative and Qualitative Variables. 10.1038/s41598-020-60652-9
  • Julian Blank et al. (2020). Pymoo: Multi-Objective Optimization in Python. 10.1109/access.2020.2990567
  • Jasper Snoek et al. (2012). Practical Bayesian Optimization of Machine Learning Algorithms. W2950182411

topology / optimization / structural

This cluster covers systematic methods for optimizing structural designs and material topologies to achieve specific performance objectives.

• 使う op: vol_euler_number(連結性(閉じた空隙の数)、voxel → measurement)、vol_wall_thickness(最小肉厚、voxel → signal)、edt_jfa(距離変換(肉厚・隙間)、voxel → sdf)、vol_granulometry(空隙の大きさ分布、voxel → measurement)、mesh_volume(体積(材料量)、mesh → measurement)、inertia_tensor(慣性テンソル、points → matrix)、ほか 19 個: principal_moments、doe_orthogonal_array、doe_fractional_factorial、doe_central_composite、doe_box_behnken、doe_lhs、doe_encode、doe_decode、doe_main_effects、rsm_fit、mv_pls、mv_pca、feat_image_vector、doe_morris、sens_morris、doe_saltelli、sens_sobol、mv_confounding、mv_residual_pca
• 代表論文(新しい順 + いちばん古い 1 本):
  • Tianshu Tang et al. (2024). Topology Optimization: A Review for Structural Designs Under Statics Problems. 10.3390/ma17235970
  • Muayad Habashneh et al. (2024). Plastic-limit probabilistic structural topology optimization of steel beams. 10.1016/j.apm.2024.01.029
  • Muayad Habashneh et al. (2023). Optimizing structural topology design through consideration of fatigue crack propagation. 10.1016/j.cma.2023.116629
  • Yunzhen He et al. (2023). A hole-filling based approach to controlling structural complexity in topology optimization. 10.1016/j.cma.2023.116391
  • Yafeng Wang et al. (2023). Multi-material topology optimization for maximizing structural stability under thermo-mechanical loading. 10.1016/j.cma.2023.115938
  • Vipul Mehta et al. (2012). Two-Step Design of Multicontact-Aided Cellular Compliant Mechanisms for Stress Relief. 10.1115/1.4007694

magnetic / torque / electric

This cluster covers the design, optimization, and control of magnetic gear systems and composite motor architectures that combine magnetic gearing with electric machines.

• 使う op: displacement_series(変位の時系列、video → pairs)、phase_displacement(位相ベースの微小変位、video → table)、riesz_displacement_series(Riesz 変換の変位、video → pairs)、temporal_band_power(帯域ごとの動きの量、video → image2d)、band_snr(動きの SN、video → table)、order_spectrum(回転次数、signal → table)、ほか 2 個: angular_resample、piv_multipass
• 代表論文(新しい順 + いちばん古い 1 本):
  • Hangyeol Song et al. (2023). Magnetic Gear-Based Actuator: A Framework of Design, Optimization, and Disturbance Observer-Based Torque Control. 10.1109/lra.2023.3313011
  • Libing Jing et al. (2023). Design and Optimization of Coaxial Magnetic Gear With Double-Layer PMs and Spoke Structure for Tidal Power Generation. 10.1109/tmech.2023.3261987
  • Gerardo Ruiz-Ponce et al. (2023). Design Optimization of an Axial Flux Magnetic Gear by Using Reluctance Network Modeling and Genetic Algorithm. 10.3390/en16041852
  • Xiao-bin XU et al. (2023). Design and Optimization of Axial Field Flux-Switching Magnetic Gear Composite Motor Based on Varying-Network Magnetic Circuit. 10.1109/access.2023.3275444
  • Changwoo Lee et al. (2022). Topology Optimization for the Manufacturable and Structurally Safe Synchronous Reluctance Motors With Multiple Iron Webs and Bridges. 10.1109/tie.2022.3148751
  • Michinari Fukuoka et al. (2012). A method for optimizing the design of SPM type magnetic gear based on reluctance network analysis. 10.1109/icelmach.2012.6349834

algorithms / optimization / problems

This cluster covers optimization algorithms and mathematical methods for solving complex computational problems, ranging from classical gradient-based approaches to modern metaheuristic and evolutionary strategies.

• 使う op: vol_euler_number(連結性(閉じた空隙の数)、voxel → measurement)、vol_wall_thickness(最小肉厚、voxel → signal)、edt_jfa(距離変換(肉厚・隙間)、voxel → sdf)、vol_granulometry(空隙の大きさ分布、voxel → measurement)、mesh_volume(体積(材料量)、mesh → measurement)、inertia_tensor(慣性テンソル、points → matrix)、ほか 19 個: principal_moments、doe_orthogonal_array、doe_fractional_factorial、doe_central_composite、doe_box_behnken、doe_lhs、doe_encode、doe_decode、doe_main_effects、rsm_fit、mv_pls、mv_pca、feat_image_vector、doe_morris、sens_morris、doe_saltelli、sens_sobol、mv_confounding、mv_residual_pca
• 代表論文(新しい順 + いちばん古い 1 本):
  • Sunday O. Oladejo et al. (2024). The Hiking Optimization Algorithm: A novel human-based metaheuristic approach. 10.1016/j.knosys.2024.111880
  • Kanchan Rajwar et al. (2023). An exhaustive review of the metaheuristic algorithms for search and optimization: taxonomy, applications, and open challenges. 10.1007/s10462-023-10470-y
  • Jing Liang et al. (2022). A Survey on Evolutionary Constrained Multiobjective Optimization. 10.1109/tevc.2022.3155533
  • E Weinan et al. (2021). Algorithms for solving high dimensional PDEs: from nonlinear Monte Carlo to machine learning. 10.1088/1361-6544/ac337f
  • Jun Tang et al. (2021). A Review on Representative Swarm Intelligence Algorithms for Solving Optimization Problems: Applications and Trends. 10.1109/jas.2021.1004129
  • Luis Miguel Rios et al. (2012). Derivative-free optimization: a review of algorithms and comparison of software implementations. 10.1007/s10898-012-9951-y

surrogate / optimization / algorithms

This cluster covers surrogate modeling and optimization algorithms used to efficiently solve computationally expensive design and simulation problems.

• 使う op: vol_euler_number(連結性(閉じた空隙の数)、voxel → measurement)、vol_wall_thickness(最小肉厚、voxel → signal)、edt_jfa(距離変換(肉厚・隙間)、voxel → sdf)、vol_granulometry(空隙の大きさ分布、voxel → measurement)、mesh_volume(体積(材料量)、mesh → measurement)、inertia_tensor(慣性テンソル、points → matrix)、ほか 19 個: principal_moments、doe_orthogonal_array、doe_fractional_factorial、doe_central_composite、doe_box_behnken、doe_lhs、doe_encode、doe_decode、doe_main_effects、rsm_fit、mv_pls、mv_pca、feat_image_vector、doe_morris、sens_morris、doe_saltelli、sens_sobol、mv_confounding、mv_residual_pca
• 代表論文(新しい順 + いちばん古い 1 本):
  • Ankush Kapoor et al. (2025). A Surrogate-Assisted Multiconcept Optimization Framework for Real-World Engineering Design. 10.1115/1.4068404
  • Youfa Fu et al. (2024). Secretary bird optimization algorithm: a new metaheuristic for solving global optimization problems. 10.1007/s10462-024-10729-y
  • Xiaopeng Wang et al. (2024). Artificial Protozoa Optimizer (APO): A novel bio-inspired metaheuristic algorithm for engineering optimization. 10.1016/j.knosys.2024.111737
  • Shervin Zakeri et al. (2023). A decision analysis model for material selection using simple ranking process. 10.1038/s41598-023-35405-z
  • Nayan Sarker et al. (2023). Applications of Machine Learning and Deep Learning in Antenna Design, Optimization, and Selection: A Review. 10.1109/access.2023.3317371
  • Xiaojian Zhou et al. (2012). Ensemble of Surrogates for Dual Response Surface Modeling in Robust Parameter Design. 10.1002/qre.1298

robot / robotic / control

239 本

This cluster covers the design, actuation, and control of robotic systems ranging from compliant mechanisms to soft robots. It encompasses mechanical engineering principles, actuator technologies, and locomotion strategies that enable robots to interact safely with humans and dynamic environments.

• Soft robotics fundamentals: Pneumatic networks, dielectric elastomer actuators, and flexible materials enable compliant, safe robot designs with integrated actuation and sensing
• Actuator technologies: Piezoelectric, pneumatic, and bistable actuators provide diverse control modalities for different robotic scales and applications
• Compliant mechanisms: Flexible structures that achieve motion through material deformation rather than rigid joints, reducing complexity and improving human-robot safety
• Gripper design: Soft gripping strategies enable adaptive grasping through actuation control, geometry, and smart material selection
• Locomotion strategies: Crawling, undulation, and other gaits leverage soft body dynamics for navigation in constrained and unstructured environments

使う op(テーマ → op の対応表から。ノートで型と例を確かめてから使う):

• アクチュエータ(制御は外、動きを画像で測る側): displacement_series(変位の時系列、video → pairs)、phase_displacement(位相ベースの微小変位、video → table)、riesz_displacement_series(Riesz 変換の変位、video → pairs)、temporal_band_power(帯域ごとの動きの量、video → image2d)、band_snr(動きの SN、video → table)、order_spectrum(回転次数、signal → table)、angular_resample(回転同期、signal → table)、piv_multipass(流体(油空圧)の流れ場、image2d × image2d → flow2d)
• ロボット(姿勢・位置合わせ・動き) —— 入口: docs/PERCEPTION_PHYSICAL_AI.md
• ロボット(姿勢・位置合わせ・動き): pnp_ransac(カメラから物体の姿勢、points × keypoints → pose)、dlt_pose(姿勢の直接解、points × keypoints → pose)、reprojection_error(姿勢の検証、points × keypoints → measurement)、optimize_pose_graph(軌跡の整合、pose → table)、relative_pose(相対姿勢、pose × pose → pose)、register_cpd_rigid(剛体の位置合わせ、points × points → pose)、icp_point2plane(ICP、points × points × normals → pose)、estimate_flow(3D の流れ、points × points → flow_scattered)、segment_rigid_motions(動く部品の分離、points × points → labels)、fit_rigid(剛体運動の当てはめ、points × points → pose)、peg_offset_from_rgbd(peg-in-hole: 手首カメラの RGB-D からずれ、rgb × image2d × matrix × matrix → table)、hole_centre_from_rgbd(穴の中心、rgb × image2d × matrix → table)、jamming_diagram(かじり(jamming)の図(Whitney)、table → table)、insertion_failure_classify(挿入の失敗の分類、table → table)、spiral_search_points(穴探しのらせん探索、scalar × scalar × scalar → table)、slip_predict(不整地の車輪の滑りの予測、table × signal → table)、dem_slope(地形の高さ地図から傾斜、depth → image2d)
• 触覚センサ(接触・圧力・ずれ): tac_contact_mask(接触域、image → region)、tac_pressure_proxy(圧力の代理量、image → image)、tac_shear_field(ずれ場、image → image)、tac_surface_normal(接触面の法線、image → image)、tac_height_from_shading(陰影からの高さ、image → image)、hertz_force(Hertz 接触の力と押し込み(閉形式)、scalar × scalar → scalar)、contact_radius_fit(接触半径から押し込み力、normalmap × matrix × matrix × scalar × scalar → table)、mindlin_partial_slip(部分すべり(Mindlin)の固着域、scalar × table × scalar × scalar × scalar → table)、marker_track(膜のマーカーを追う、image2d × image2d × scalar × scalar × scalar → table)、membrane_shear_field(マーカーの変位 → ずれ場、table × table × matrix × matrix × table → table)、torque_decompose(ねじりと傾きのトルクを分ける、matrix × matrix × scalar × scalar × scalar → table)、gradient_lut_invert(GelSight 型の色 → 法線の表引き、rgb × table × image2d → normalmap)、large_deformation_inverse(大変形の接触の逆問題、scalar × scalar × table → table)
• 機械要素(歯車・ねじ・機構): polar_unwrap(歯車の極座標展開(歯形)、image2d → image2d)、cylinder_unwrap(円筒面の展開(ねじ山)、voxel → voxel)、m1_measure_pairs(歯厚・ピッチ、image → feature)、edges_sub_pix(歯形の輪郭、image → contour)、envelope_spectrum(歯車の噛み合い振動、signal → table)

kinematics / manipulator / robotic

This cluster covers the mathematical and computational foundations for analyzing and controlling robotic manipulators through kinematic modeling.

• 使う op: pnp_ransac(カメラから物体の姿勢、points × keypoints → pose)、dlt_pose(姿勢の直接解、points × keypoints → pose)、reprojection_error(姿勢の検証、points × keypoints → measurement)、optimize_pose_graph(軌跡の整合、pose → table)、relative_pose(相対姿勢、pose × pose → pose)、register_cpd_rigid(剛体の位置合わせ、points × points → pose)、ほか 11 個: icp_point2plane、estimate_flow、segment_rigid_motions、fit_rigid、peg_offset_from_rgbd、hole_centre_from_rgbd、jamming_diagram、insertion_failure_classify、spiral_search_points、slip_predict、dem_slope
• 代表論文(新しい順 + いちばん古い 1 本):
  • Ana Calzada-García et al. (2025). A Review on Inverse Kinematics, Control and Planning for Robotic Manipulators With and Without Obstacles via Deep Neural Networks. 10.3390/a18010023
  • Yiming Li et al. (2024). Configuration Space Distance Fields for Manipulation Planning. 10.15607/rss.2024.xx.131
  • Zhengtai Xie et al. (2023). A Biobjective Scheme for Kinematic Control of Mobile Robotic Arms With Manipulability Optimization. 10.1109/tmech.2023.3313516
  • Zhengtai Xie et al. (2023). A Bi-Criteria Kinematic Strategy for Motion/Force Control of Robotic Manipulator. 10.1109/tase.2023.3313564
  • Yuchuang Tong et al. (2023). Adaptive Tracking Control of Robotic Manipulators With Unknown Kinematics and Uncertain Dynamics. 10.1109/tase.2023.3309964
  • Luiz A. Radavelli et al. (2012). A Comparative Study of the Kinematics of Robots Manipulators by Denavit-Hartenberg and Dual Quaternion. W2796076341

linkage / bar / mechanisms

This cluster addresses the synthesis, analysis, and design of linkage mechanisms—particularly four-bar, six-bar, and eight-bar configurations—used in robotic systems, deployable structures, and motion generation.

• 使う op: polar_unwrap(歯車の極座標展開(歯形)、image2d → image2d)、cylinder_unwrap(円筒面の展開(ねじ山)、voxel → voxel)、m1_measure_pairs(歯厚・ピッチ、image → feature)、edges_sub_pix(歯形の輪郭、image → contour)、envelope_spectrum(歯車の噛み合い振動、signal → table)、order_spectrum(次数スペクトル、signal → table)、ほか 2 個: segment_rigid_motions、displacement_series
• 代表論文(新しい順 + いちばん古い 1 本):
  • Enrique Soriano Heras et al. (2024). Mathematical Dimensional Synthesis of Four-Bar Linkages Based on Cognate Mechanisms. 10.3390/math13010011
  • Sumin Lee et al. (2024). Deep generative model-based synthesis framework of four-bar linkage mechanisms with target conditions. 10.1093/jcde/qwae084
  • Asok Kumar Mallik et al. (2021). Kinematic Analysis and Synthesis of Mechanisms. 10.1201/9780429327278
  • Volkert van der Wijk (2020). The Grand 4R Four-Bar Based Inherently Balanced Linkage Architecture for synthesis of shaking force balanced and gravity force balanced mechanisms. 10.1016/j.mechmachtheory.2020.103815
  • Haohua Xiu et al. (2019). Synthesis and analysis of Fulleroid-like deployable Archimedean mechanisms based on an overconstrained eight-bar linkage. 10.1016/j.mechmachtheory.2019.03.004
  • Sang Jun Nam et al. (2012). The Spring-Connected Rigid Block Model Based Automatic Synthesis of Planar Linkage Mechanisms: Numerical Issues and Remedies. 10.1115/1.4006266

surgical / kinematic / invasive

This cluster covers the kinematic design, mechanical optimization, and control systems for minimally invasive surgical robots, particularly focusing on remote-center-of-motion (RCM) mechanisms and master-slave manipulation architectures.

• 代表論文(新しい順 + いちばん古い 1 本):
  • Botao Lin et al. (2022). A Modular Lockable Mechanism for Tendon-Driven Robots: Design, Modeling and Characterization. 10.1109/lra.2022.3142907
  • José Cornejo et al. (2021). Mechanical and Kinematic Design of Surgical Mini Robotic Manipulator used into SP-LAP Multi-DOF Platform for Training and Simulation. 10.1109/intercon52678.2021.9532965
  • Theodosia Lourdes Thomas et al. (2021). Surgical Applications of Compliant Mechanisms: A Review. 10.1115/1.4049491
  • Tianqi Xu et al. (2019). Millimeter-scale flexible robots with programmable three-dimensional magnetization and motions. 10.1126/scirobotics.aav4494
  • Yunlei Liang et al. (2019). Mechanism Design and Optimization of a Haptic Master Manipulator for Laparoscopic Surgical Robots. 10.1109/access.2019.2943622
  • Chin‐Hsing Kuo et al. (2012). Kinematic design considerations for minimally invasive surgical robots: an overview. 10.1002/rcs.453

compliant / mechanism / mechanisms

This cluster covers the design, modeling, and optimization of compliant mechanisms—mechanical structures that achieve motion and force transformation through elastic deformation rather than rigid body articulation.

• 使う op: polar_unwrap(歯車の極座標展開(歯形)、image2d → image2d)、cylinder_unwrap(円筒面の展開(ねじ山)、voxel → voxel)、m1_measure_pairs(歯厚・ピッチ、image → feature)、edges_sub_pix(歯形の輪郭、image → contour)、envelope_spectrum(歯車の噛み合い振動、signal → table)、order_spectrum(次数スペクトル、signal → table)、ほか 2 個: segment_rigid_motions、displacement_series
• 代表論文(新しい順 + いちばん古い 1 本):
  • Kamil Kocak et al. (2023). Design of a compliant lever-type passive vibration isolator with quasi-zero-stiffness mechanism. 10.1016/j.jsv.2023.117758
  • Mingxiang Ling et al. (2022). Dynamic Design of a Novel High-Speed Piezoelectric Flow Control Valve Based on Compliant Mechanism. 10.1109/tmech.2022.3169761
  • Shrishail B. Sollapur et al. (2022). Design and Experimental Investigation of XY Compliant Mechanism for Precision Applications. 10.1149/10701.4967ecst
  • Tinghao Liu et al. (2022). Design of Deployable Structures by Using Bistable Compliant Mechanisms. 10.3390/mi13050651
  • Jincheng Guo et al. (2021). Stiffness-Oriented Structure Topology Optimization for Hinge-Free Compliant Mechanisms Design. 10.3390/app112210831
  • — (2013). Handbook of Compliant Mechanisms. 10.1002/9781118516485

compliant / mechanism / force

This cluster covers the design, modeling, and optimization of compliant mechanisms—structures that achieve motion through elastic deformation rather than rigid joints.

• 使う op: polar_unwrap(歯車の極座標展開(歯形)、image2d → image2d)、cylinder_unwrap(円筒面の展開(ねじ山)、voxel → voxel)、m1_measure_pairs(歯厚・ピッチ、image → feature)、edges_sub_pix(歯形の輪郭、image → contour)、envelope_spectrum(歯車の噛み合い振動、signal → table)、order_spectrum(次数スペクトル、signal → table)、ほか 2 個: segment_rigid_motions、displacement_series
• 代表論文(新しい順 + いちばん古い 1 本):
  • Zekui Lyu et al. (2023). Design of a Compliant Vertical Micropositioning Stage Based on Lamina Emergent Mechanisms. 10.1109/tmech.2023.3235336
  • Tinghao Liu et al. (2022). Design of a Cylindrical Compliant Linear Guide with Decoupling Parallelogram Mechanisms. 10.3390/mi13081275
  • Jan Dittli et al. (2022). Design of a compliant, stabilizing wrist mechanism for a pediatric hand exoskeleton. 10.1109/icorr55369.2022.9896550
  • Ke Xü et al. (2022). Design of a Compliant Flapping-Wing Mechanism With Flapping–Twist–Swing Motion. 10.1109/tmech.2022.3175884
  • Bingxiao Ding et al. (2022). Configuration design and experimental verification of a variable constant-force compliant mechanism. 10.1017/s0263574722000340
  • Allen B. Mackay et al. (2012). Metrics for Evaluation and Design of Large-Displacement Linear-Motion Compliant Mechanisms. 10.1115/1.4004191

robot / robots / robotic

This cluster encompasses robotic system design, control, and navigation technologies including mobile robot path planning, locomotion mechanisms, manipulation systems, and human-robot interaction.

• 使う op: pnp_ransac(カメラから物体の姿勢、points × keypoints → pose)、dlt_pose(姿勢の直接解、points × keypoints → pose)、reprojection_error(姿勢の検証、points × keypoints → measurement)、optimize_pose_graph(軌跡の整合、pose → table)、relative_pose(相対姿勢、pose × pose → pose)、register_cpd_rigid(剛体の位置合わせ、points × points → pose)、ほか 11 個: icp_point2plane、estimate_flow、segment_rigid_motions、fit_rigid、peg_offset_from_rgbd、hole_centre_from_rgbd、jamming_diagram、insertion_failure_classify、spiral_search_points、slip_predict、dem_slope
• 代表論文(新しい順 + いちばん古い 1 本):
  • Tianhui Sun et al. (2025). Bio-inspired controllable adhesion for robotics: mechanisms, design, and future directions. 10.1017/s0263574725101793
  • Muhammad Faizan Shah et al. (2023). Mechanism Design and Control of Shoulder Rehabilitation Robots: A Review. 10.1109/tmrb.2023.3310086
  • Zhuo Wang et al. (2023). Optimization Design and Performance Analysis of a Bionic Knee Joint Based on the Geared Five-Bar Mechanism. 10.3390/bioengineering10050582
  • Mikhail Koptev et al. (2022). Neural Joint Space Implicit Signed Distance Functions for Reactive Robot Manipulator Control. 10.1109/lra.2022.3227860
  • Young-Ha Shin et al. (2022). Design of KAIST HOUND, a Quadruped Robot Platform for Fast and Efficient Locomotion with Mixed-Integer Nonlinear Optimization of a Gear Train. 10.1109/icra46639.2022.9811755
  • Bilge Mutlu et al. (2012). Conversational gaze mechanisms for humanlike robots. 10.1145/2070719.2070725

soft / robotics / robots

This cluster covers the design, actuation, and control mechanisms of soft robots—compliant systems built from elastomeric and flexible materials that enable safe human interaction and adaptability in unstructured environments.

• 使う op: tac_contact_mask(接触域、image → region)、tac_pressure_proxy(圧力の代理量、image → image)、tac_shear_field(ずれ場、image → image)、tac_surface_normal(接触面の法線、image → image)、tac_height_from_shading(陰影からの高さ、image → image)、hertz_force(Hertz 接触の力と押し込み(閉形式)、scalar × scalar → scalar)、ほか 24 個: contact_radius_fit、mindlin_partial_slip、marker_track、membrane_shear_field、torque_decompose、gradient_lut_invert、large_deformation_inverse、pnp_ransac、dlt_pose、reprojection_error、optimize_pose_graph、relative_pose、register_cpd_rigid、icp_point2plane、estimate_flow、segment_rigid_motions、fit_rigid、peg_offset_from_rgbd、hole_centre_from_rgbd、jamming_diagram、insertion_failure_classify、spiral_search_points、slip_predict、dem_slope
• 代表論文(新しい順 + いちばん古い 1 本):
  • Weiqiang Dou et al. (2023). Design and Modeling of a Hybrid Soft Robotic Manipulator With Compliant Mechanism. 10.1109/lra.2023.3248485
  • Öncay Yaşa et al. (2022). An Overview of Soft Robotics. 10.1146/annurev-control-062322-100607
  • Yinding Chi et al. (2022). Bistable and Multistable Actuators for Soft Robots: Structures, Materials, and Functionalities. 10.1002/adma.202110384
  • Yoonho Kim et al. (2022). Magnetic Soft Materials and Robots. 10.1021/acs.chemrev.1c00481
  • Matheus S. Xavier et al. (2020). Finite Element Modeling of Soft Fluidic Actuators: Overview and Recent Developments. 10.1002/aisy.202000187
  • Bobak Mosadegh et al. (2014). Pneumatic Networks for Soft Robotics that Actuate Rapidly. 10.1002/adfm.201303288

robot / robots / mechanism

This cluster covers the design, control, and implementation of robotic mechanisms across various morphologies and locomotion modes, including biomimetic systems that replicate animal movement patterns.

• 使う op: pnp_ransac(カメラから物体の姿勢、points × keypoints → pose)、dlt_pose(姿勢の直接解、points × keypoints → pose)、reprojection_error(姿勢の検証、points × keypoints → measurement)、optimize_pose_graph(軌跡の整合、pose → table)、relative_pose(相対姿勢、pose × pose → pose)、register_cpd_rigid(剛体の位置合わせ、points × points → pose)、ほか 18 個: icp_point2plane、estimate_flow、segment_rigid_motions、fit_rigid、peg_offset_from_rgbd、hole_centre_from_rgbd、jamming_diagram、insertion_failure_classify、spiral_search_points、slip_predict、dem_slope、polar_unwrap、cylinder_unwrap、m1_measure_pairs、edges_sub_pix、envelope_spectrum、order_spectrum、displacement_series
• 代表論文(新しい順 + いちばん古い 1 本):
  • Guanqi Liang et al. (2024). Decoding modular reconfigurable robots: A survey on mechanisms and design. 10.1177/02783649241283847
  • Omer Saleem et al. (2023). Fuzzy-Immune-Regulated Adaptive Degree-of-Stability LQR for a Self-Balancing Robotic Mechanism: Design and HIL Realization. 10.1109/lra.2023.3286176
  • Xiaomeng Liu et al. (2023). Multi-Branch Cellular Space Robot Mechanism Design and Climbing Behavior Research. 10.1115/1.4056602
  • Jizhuang Fan et al. (2022). Design of the Jump Mechanism for a Biomimetic Robotic Frog. 10.3390/biomimetics7040142
  • Hobyeong Chae et al. (2022). A Tethered Façade Cleaning Robot Based on a Dual Rope Windlass Climbing Mechanism: Design and Experiments. 10.1109/tmech.2022.3172689
  • Fengyu Xu et al. (2012). Design and Analysis of a Wall-Climbing Robot Based on a Mechanism Utilizing Hook-Like Claws. 10.5772/53895

additive / manufacturing / printing

318 本

This cluster covers the full spectrum of additive manufacturing (AM) and 3D printing technologies, from material science and process optimization to structural design and applications across multiple industries. It addresses how layer-by-layer fabrication enables creation of complex geometries, lattice structures, and composite materials that would be difficult or impossible to produce through traditional manufacturing methods.

• Material diversity in AM: Polymers, ceramics, composites, and metals can be additively manufactured with tailored mechanical properties; preceramic polymers (PCPs) enable complex ceramic part fabrication
• Topology optimization: Design of lattice and spinodal architected structures with tunable anisotropy, porosity, and orientation to optimize performance across multiple scales
• Process control and AI integration: Closed-loop 3D printing with AI-augmented quality monitoring addresses parameter sensitivity (print speed, temperature) that can introduce structural defects
• Composite reinforcement: Continuous fiber-reinforced thermoset composites and multi-axis robotic integration enhance design flexibility and scalability
• High-mass loading electrodes: Nanostructured materials in energy storage applications achieve improved electrochemical performance through 3D printing geometry optimization

使う op(テーマ → op の対応表から。ノートで型と例を確かめてから使う):

• 付加製造(スライス・G-code・層検査): mesh_slice_contours(メッシュを層の輪郭に、mesh → table)、mesh_slice_stack(層マスクの積み、mesh → voxel)、contours_to_gcode(輪郭 → G-code、table → table)、gcode_read(G-code を線分の表に、text → table)、gcode_write(表 → G-code、table × text → text)、gcode_layer_image(層の期待画像、table → image2d)、gcode_extrusion_volume(押し出し量、table → measurement)、gcode_time_estimate(所要時間、table → measurement)、print_layer_defect_map(層画像の欠け・はみ出し、image2d × image2d → image2d)、read_3mf(3MF 読み、text → mesh)、write_3mf(3MF 書き、text × mesh → text)、vol_wall_thickness(造形物 CT の肉厚、voxel → signal)
• トポロジー最適化・格子構造の評価 —— 入口: 進化エンジン(docs/ENGINE.md、docs/EVOLUTION_ENVIRONMENT.md)は設計パラメータを適応度で回す側
• トポロジー最適化・格子構造の評価: vol_euler_number(連結性(閉じた空隙の数)、voxel → measurement)、edt_jfa(距離変換(肉厚・隙間)、voxel → sdf)、vol_granulometry(空隙の大きさ分布、voxel → measurement)、mesh_volume(体積(材料量)、mesh → measurement)、inertia_tensor(慣性テンソル、points → matrix)、principal_moments(主慣性モーメント、points → descriptor)
• in-situ 監視(メルトプール・高速動画): temporal_bandpass(動画の時間帯域(揺らぎ)、video → video)、motion_magnify(微小な動きの拡大、video → table)、video_spacetime_cube(時空間の立方体、video → voxel)、video_summary_keyframes(要約コマ、video → indices)、blob_count(スパッタ・粒の数、region → feature)、area_center(溶融池の面積と重心、region → match)、volseq_magnify_motion(3D+t の拍動の拡大、volseq → volseq)、focus_sweep_height_video(焦点掃引 → 高さの時系列、volseq → video)
• 材料(見え方・組織・表面): spectrum_to_srgb(分光 → 色、signal → vector)、thin_film_reflectance(薄膜の干渉色、signal → signal)、material_catalog(材質の外観表、入力なし → table)、oren_nayar(粗い面の反射、normalmap → image2d)、brdf_microfacet(微小面の反射、normalmap → image2d)、metallic_flake_normals(メタリック塗装、入力なし → normalmap)、corrosion_mask(腐食域、入力なし → image2d)、cooc_feature_matrix(組織のテクスチャ、image → feature)、gabor(方向性のテクスチャ、image → image)、entropy_image(組織の乱れ、image → image)、material_table(材料の光学物性表(83 種、光学系と観察対象が同じ表を引く)、入力なし → table)、material_optical_constants(波長ごとの n, k、入力なし → table)、material_transmittance(厚さ d の透過率、入力なし → pairs)、thermo_optic_coefficient(温度で変わる屈折率、入力なし → table)、phase_fractions(粉末 X 線回折から相の割合、signal × signal × table → table)、scherrer_size(回折線の幅から結晶子の大きさ(Scherrer)、scalar × scalar × scalar → scalar)
• 粉体(粒度・流動性・粉砕): particle_size_dx(粒度分布の Dx(D10 / D50 / D90)、table × scalar → scalar)、particle_image_d50(粒の画像から D50、image2d × scalar → table)、comminution_law_fit(粉砕のエネルギー則(Bond ほか)の当てはめ、signal × signal → table)、powder_flowability_class(安息角から流動性の区分、scalar → table)、repose_angle_heightmap(高さ地図から安息角、image2d × scalar → table)、hopper_discharge_rate(ホッパの排出流量(Beverloo)、signal × signal × scalar → table)

lattice / structures / additive

This cluster covers the design, manufacturing, and performance analysis of lattice and cellular structures produced through additive manufacturing (AM) techniques.

• 使う op: vol_euler_number(連結性(閉じた空隙の数)、voxel → measurement)、vol_wall_thickness(最小肉厚、voxel → signal)、edt_jfa(距離変換(肉厚・隙間)、voxel → sdf)、vol_granulometry(空隙の大きさ分布、voxel → measurement)、mesh_volume(体積(材料量)、mesh → measurement)、inertia_tensor(慣性テンソル、points → matrix)、ほか 12 個: principal_moments、mesh_slice_contours、mesh_slice_stack、contours_to_gcode、gcode_read、gcode_write、gcode_layer_image、gcode_extrusion_volume、gcode_time_estimate、print_layer_defect_map、read_3mf、write_3mf
• 代表論文(新しい順 + いちばん古い 1 本):
  • Jiuyi Li et al. (2025). Additively manufactured metallic TPMS lattice structures: design strategies, fabrication, multifunctional properties, and applications. 10.1038/s44334-025-00057-6
  • Jinlin Yang et al. (2025). Additive Manufacturing and Influencing Factors of Lattice Structures: A Review. 10.3390/ma18071397
  • Xuyu Zhang et al. (2025). A review of structural topology optimization for fiber-reinforced composites. 10.1016/j.compositesb.2025.112393
  • Yuhua Li et al. (2024). High Mechanical Performance of Lattice Structures Fabricated by Additive Manufacturing. 10.3390/met14101165
  • Numan Khan et al. (2024). A systematic review of design for additive manufacturing of aerospace lattice structures: Current trends and future directions. 10.1016/j.paerosci.2024.101021
  • Asliah Seharing et al. (2014). A review on integration of lightweight gradient lattice structures in additive manufacturing parts. 10.1177/1687814020916951

printing / additive / manufacturing

This cluster covers advanced additive manufacturing and 3D printing technologies, including robotic systems, multi-axis control strategies, and material extrusion methods for creating composite and metal components.

• 使う op: mesh_slice_contours(メッシュを層の輪郭に、mesh → table)、mesh_slice_stack(層マスクの積み、mesh → voxel)、contours_to_gcode(輪郭 → G-code、table → table)、gcode_read(G-code を線分の表に、text → table)、gcode_write(表 → G-code、table × text → text)、gcode_layer_image(層の期待画像、table → image2d)、ほか 6 個: gcode_extrusion_volume、gcode_time_estimate、print_layer_defect_map、read_3mf、write_3mf、vol_wall_thickness
• 代表論文(新しい順 + いちばん古い 1 本):
  • Muhammad Sayeed Mahmud et al. (2025). A COMPREHENSIVE ANALYSIS OF NON-PLANAR TOOLPATH OPTIMIZATION IN MULTI-AXIS 3D PRINTING: EVALUATING THE EFFICIENCY OF CURVED LAYER SLICING STRATEGIES. 10.63125/5fdxa722
  • Tianyu Zhang et al. (2025). Toolpath generation for high density spatial fiber printing guided by principal stresses. 10.1016/j.compositesb.2025.112154
  • Jaime Gould et al. (2024). TRAvel Slicer: Continuous Extrusion Toolpaths for 3D Printing. 10.1145/3654777.3676349
  • Yefan Zhi et al. (2024). Designing 3D-printed concrete structures with scaled fabrication models. 10.1007/s44223-024-00070-3
  • Arif M. Abdullah et al. (2024). Robotic 3D Printing of Continuous Fiber Reinforced Thermoset Composites. 10.1002/admt.202400839
  • Ezekiel G. Merriam et al. (2014). Design of 3D-Printed Titanium Compliant Mechanisms. W787954873

printing / additive / manufacturing

This cluster covers additive manufacturing (3D printing) technologies and their applications across materials science, biomedical engineering, construction, and energy storage.

• 使う op: mesh_slice_contours(メッシュを層の輪郭に、mesh → table)、mesh_slice_stack(層マスクの積み、mesh → voxel)、contours_to_gcode(輪郭 → G-code、table → table)、gcode_read(G-code を線分の表に、text → table)、gcode_write(表 → G-code、table × text → text)、gcode_layer_image(層の期待画像、table → image2d)、ほか 6 個: gcode_extrusion_volume、gcode_time_estimate、print_layer_defect_map、read_3mf、write_3mf、vol_wall_thickness
• 代表論文(新しい順 + いちばん古い 1 本):
  • Seulhee Kwon et al. (2025). Understanding and Resolving 3D Printing Challenges: A Systematic Literature Review. 10.3390/pr13061772
  • Ivan Malashin et al. (2024). Machine Learning in 3D and 4D Printing of Polymer Composites: A Review. 10.3390/polym16223125
  • Bilal Cinici et al. (2024). Fabrication Strategies for Bioceramic Scaffolds in Bone Tissue Engineering with Generative Design Applications. 10.3390/biomimetics9070409
  • Zibo Zuo et al. (2024). Strategies towards large-scale 3D printing without size constraints. 10.1080/17452759.2024.2346821
  • Longfei Zhou et al. (2024). Additive Manufacturing: A Comprehensive Review. 10.3390/s24092668
  • Benjamin Vayre et al. (2012). Metallic additive manufacturing: state-of-the-art review and prospects. 10.1051/meca/2012003

additive / manufacturing / printing

This cluster covers additive manufacturing (3D printing) technologies, processes, and applications across industrial sectors.

• 使う op: mesh_slice_contours(メッシュを層の輪郭に、mesh → table)、mesh_slice_stack(層マスクの積み、mesh → voxel)、contours_to_gcode(輪郭 → G-code、table → table)、gcode_read(G-code を線分の表に、text → table)、gcode_write(表 → G-code、table × text → text)、gcode_layer_image(層の期待画像、table → image2d)、ほか 6 個: gcode_extrusion_volume、gcode_time_estimate、print_layer_defect_map、read_3mf、write_3mf、vol_wall_thickness
• 代表論文(新しい順 + いちばん古い 1 本):
  • Fatih Altun et al. (2025). AI-Driven Innovations in 3D Printing: Optimization, Automation, and Intelligent Control. 10.3390/jmmp9100329
  • Edoardo Risaliti et al. (2025). Optimizing Lightweight Material Selection in Automotive Engineering: A Hybrid Methodology Incorporating Ashby’s Method and VIKOR Analysis. 10.3390/machines13010063
  • Shutian Liu et al. (2024). A Survey of Topology Optimization Methods Considering Manufacturable Structural Feature Constraints for Additive Manufacturing Structures. 10.1016/j.amf.2024.200143
  • Xiaonan Wang et al. (2024). Concrete 3D printing technology for sustainable construction: A review on raw material, concrete type and performance. 10.1016/j.dibe.2024.100378
  • Chan Soo Ha et al. (2023). Rapid inverse design of metamaterials based on prescribed mechanical behavior through machine learning. 10.1038/s41467-023-40854-1
  • Kaufui V. Wong et al. (2012). A Review of Additive Manufacturing. 10.5402/2012/208760

manufacturing / additive / powder

This cluster covers advanced manufacturing techniques in additive manufacturing (AM) and powder bed fusion (PBF) processes, with emphasis on design optimization, quality assurance, and post-processing methods.

• 使う op: mesh_slice_contours(メッシュを層の輪郭に、mesh → table)、mesh_slice_stack(層マスクの積み、mesh → voxel)、contours_to_gcode(輪郭 → G-code、table → table)、gcode_read(G-code を線分の表に、text → table)、gcode_write(表 → G-code、table × text → text)、gcode_layer_image(層の期待画像、table → image2d)、ほか 21 個: gcode_extrusion_volume、gcode_time_estimate、print_layer_defect_map、read_3mf、write_3mf、vol_wall_thickness、temporal_bandpass、motion_magnify、video_spacetime_cube、video_summary_keyframes、blob_count、area_center、volseq_magnify_motion、focus_sweep_height_video、particle_size_dx、particle_image_d50、comminution_law_fit、powder_flowability_class、repose_angle_heightmap、hopper_discharge_rate、vol_granulometry
• 代表論文(新しい順 + いちばん古い 1 本):
  • Shafi Khurieshi Mohammed et al. (2023). Using semantic Geometric Dimensioning and Tolerancing (GD &T) information from STEP AP242 neutral exchange files for robotic applications. 10.1007/s12008-023-01242-7
  • Mark Armstrong et al. (2022). An overview of modern metal additive manufacturing technology. 10.1016/j.jmapro.2022.10.060
  • Eujin Pei et al. (2022). A review of geometric dimensioning and tolerancing (GD&T) of additive manufacturing and powder bed fusion lattices. 10.1007/s40964-022-00304-8
  • Richard Buswell et al. (2022). Geometric quality assurance for 3D concrete printing and hybrid construction manufacturing using a standardised test part for benchmarking capability. 10.1016/j.cemconres.2022.106773
  • Germain Sossou et al. (2022). An Assembly-Oriented Design Framework for Additive Manufacturing. 10.3390/designs6010020
  • Benjamin Vayre et al. (2012). Designing for Additive Manufacturing. 10.1016/j.procir.2012.07.108

additive / fdm / manufacturing

This cluster covers additive manufacturing (AM) and fused deposition modeling (FDM) technologies, with emphasis on process optimization, material characterization, and design methodologies.

• 使う op: mesh_slice_contours(メッシュを層の輪郭に、mesh → table)、mesh_slice_stack(層マスクの積み、mesh → voxel)、contours_to_gcode(輪郭 → G-code、table → table)、gcode_read(G-code を線分の表に、text → table)、gcode_write(表 → G-code、table × text → text)、gcode_layer_image(層の期待画像、table → image2d)、ほか 6 個: gcode_extrusion_volume、gcode_time_estimate、print_layer_defect_map、read_3mf、write_3mf、vol_wall_thickness
• 代表論文(新しい順 + いちばん古い 1 本):
  • Wei Zhang et al. (2025). A static and high-cycle fatigue characterization framework of metallic lattice structures additive manufactured via fused deposition modeling based method. 10.1016/j.matdes.2025.113761
  • Abdul Rahman Sani et al. (2024). Artificial Intelligence‐Augmented Additive Manufacturing: Insights on Closed‐Loop 3D Printing. 10.1002/aisy.202400102
  • Hussein Alzyod et al. (2023). Material-Dependent Effect of Common Printing Parameters on Residual Stress and Warpage Deformation in 3D Printing: A Comprehensive Finite Element Analysis Study. 10.3390/polym15132893
  • Douglas A. J. Brion et al. (2022). Generalisable 3D printing error detection and correction via multi-head neural networks. 10.1038/s41467-022-31985-y
  • Shaoying Li et al. (2021). Collaborative optimization design of process parameter and structural topology for laser additive manufacturing. 10.1016/j.cja.2021.12.010
  • L. Villalpando et al. (2014). An Optimization Approach for Components Built by Fused Deposition Modeling with Parametric Internal Structures. 10.1016/j.procir.2014.02.050

additive / manufacturing / materials

This cluster covers the intersection of additive manufacturing (3D printing) and advanced materials science, focusing on the design, optimization, and production of complex structures with tailored properties.

• 使う op: mesh_slice_contours(メッシュを層の輪郭に、mesh → table)、mesh_slice_stack(層マスクの積み、mesh → voxel)、contours_to_gcode(輪郭 → G-code、table → table)、gcode_read(G-code を線分の表に、text → table)、gcode_write(表 → G-code、table × text → text)、gcode_layer_image(層の期待画像、table → image2d)、ほか 23 個: gcode_extrusion_volume、gcode_time_estimate、print_layer_defect_map、read_3mf、write_3mf、vol_wall_thickness、spectrum_to_srgb、thin_film_reflectance、material_catalog、oren_nayar、brdf_microfacet、metallic_flake_normals、corrosion_mask、cooc_feature_matrix、gabor、vol_granulometry、entropy_image、material_table、material_optical_constants、material_transmittance、thermo_optic_coefficient、phase_fractions、scherrer_size
• 代表論文(新しい順 + いちばん古い 1 本):
  • Bo Peng et al. (2023). Machine learning-enabled constrained multi-objective design of architected materials. 10.1038/s41467-023-42415-y
  • Fabio Distefano et al. (2023). Titanium Lattice Structures Produced via Additive Manufacturing for a Bone Scaffold: A Review. 10.3390/jfb14030125
  • Chaolin Tan et al. (2022). Mechanical property and biological behaviour of additive manufactured TiNi functionally graded lattice structure. 10.1088/2631-7990/ac94fa
  • Kadir Günaydın et al. (2022). Energy absorption enhancement of additively manufactured hexagonal and re-entrant (auxetic) lattice structures by using multi-material reinforcements. 10.1016/j.addma.2022.103076
  • Chenxi Lu et al. (2022). Architectural Design and Additive Manufacturing of Mechanical Metamaterials: A Review. 10.1016/j.eng.2021.12.023
  • Eric Wycisk et al. (2014). Effects of Defects in Laser Additive Manufactured Ti-6Al-4V on Fatigue Properties. 10.1016/j.phpro.2014.08.120

dfma / assembly / manufacturing

This cluster covers Design for Manufacturing and Assembly (DFMA) methodologies applied across construction, additive manufacturing, and industrial product design.

• 代表論文(新しい順 + いちばん古い 1 本):
  • Saeed Banihashemi et al. (2025). 3D printing in construction: sustainable technology for building industry. 10.1007/s40964-025-01314-y
  • Shuai Li et al. (2024). Frontiers in construction 3D printing: self-monitoring, multi-robot, drone-assisted processes. 10.1007/s40964-024-00794-8
  • Paul Schaechtl et al. (2024). Knowledge-based product and process design synthesis of additively manufactured non-assembly mechanisms. 10.1080/09544828.2024.2339162
  • Song Du et al. (2024). A BIM-enabled robot control system for automated integration between rebar reinforcement and 3D concrete printing. 10.1080/17452759.2024.2332423
  • Duy-Cuong Nguyen et al. (2024). BIM-based preassembly analysis for design for manufacturing and assembly of prefabricated bridges. 10.1016/j.autcon.2024.105338
  • K. Annamalai et al. (2012). Early Cost Estimate of Product during Design Stage Using Design for Manufacturing and Assembly (DFMA) Principles. 10.4028/www.scientific.net/amr.622-623.540

injection / molding / fault

147 本

This cluster covers injection molding as a manufacturing process, encompassing computer modeling, process optimization, quality control, and fault diagnosis. It addresses how advanced simulation, design optimization, and process parameter control are used to improve product quality, reduce defects, and enhance manufacturing efficiency across diverse applications from automotive to medical devices.

• Computer modeling and simulation are essential for optimizing injection molding without costly trial-and-error approaches
• Process parameters (temperature, pressure, cooling time, clamping force) directly impact dimensional accuracy, warpage, and surface quality
• Cooling system design (conformal and dynamic cooling) is critical for controlling mold temperature and reducing cycle time
• Material-specific behavior varies significantly—liquid silicone rubber, perfluoroalkoxy alkane, and other polymers require tailored process modeling
• Fault diagnosis and quality metrics include warpage, shrinkage, surface defects, and dimensional tolerances

使う op(テーマ → op の対応表から。ノートで型と例を確かめてから使う):

• 外観欠陥の検出(成形・表面): dc_local_contrast_norm(照明むらを除いた局所コントラスト、image → image)、dc_rpca_sparse(背景から外れる疎な欠陥、image → image)、defect_contrast(照明条件での欠陥コントラスト、table → table)、illumination_design(欠陥が出る照明の設計、入力なし → table)、lighting_sweep(照明を振って最良を探す、入力なし → pairs)、auto_threshold(二値化、image → region)、blob_count(欠陥の数、region → feature)、circularity(形の記述、region → feature)、sfp_normals(黒光沢・透明部品の法線を偏光から、polsweep → table)、check_difference(良品(基準)画像との差が許容を超える画素、image2d × image2d → mask)、blob_features(欠陥ごとの形の特徴量、labels2d → table)、separate_touching(接した粒・欠陥を分ける(watershed)、voxel → labels)、pod_hitmiss_fit(欠陥の大きさごとの検出確率(POD)、table → table)、varmodel_fit(良品を並べて画素ごとの許容帯を作る(HALCON の variation model、誤検出率を宣言)、video → table)、varmodel_score(許容帯の外の画素を、何 σ 外れたかと領域ごとに説明する、image2d × table → table)、anomaly_pro(欠陥図の方法を、欠陥 1 つずつの重なり(PRO / AUPRO)で比べる、video × video → table)、solid_angle_sweep(照射立体角を振って打痕・傷のコントラストの山を探す(V-ISA 型照明の立体角の設定)、入力なし → pairs)、solid_angle_render(照射立体角を絞る・広げる・ずらして、鏡面の打痕・ヘアラインの傷・梨地のムラを撮る、normalmap × table → image2d)
• SPC(管理図・工程能力): spc_xbar_r(X̄-R 管理図、matrix → table)、spc_cusum(小さなずれの累積検出、signal → table)、spc_ewma(指数平滑の管理図、signal → table)、spc_capability(Cp / Cpk、signal → table)、spc_hotelling_t2(多変量の管理、matrix → table)、null_distribution(帰無分布、signal → table)、evidence_quantile(証拠量の位置、measurement × table → table)
• 実験計画法・応答曲面・品質工学・感度解析(工程条件の決め方): doe_orthogonal_array(直交表(L4〜L27、公表された表)で条件を少ない回数で振る、入力なし → design)、doe_fractional_factorial(一部実施要因計画(分解能と交絡表つき)、入力なし → design)、doe_central_composite(応答曲面のための中心複合計画、入力なし → design)、doe_box_behnken(角を踏まない応答曲面の計画、入力なし → design)、doe_lhs(シミュレーションの入力をラテン超方格で埋める、入力なし → design)、doe_encode(条件の記録を符号化(−1..+1)へ、matrix → design)、doe_decode(計画を装置の設定値へ戻す、design → matrix)、doe_main_effects(主効果の応答表(SN 比は参考、判断は平均と散らばり)、design × signal → table)、rsm_fit(応答曲面・分散分析・停留点(支えない次数は拒否)、design × signal → table)、mv_pls(条件 ⇔ 画像の特徴量の部分最小二乗(Q² と VIP)、matrix × signal → table)、mv_pca(特徴量の主成分と独立な次元の数、matrix → table)、feat_image_vector(画像を名前と単位つきの特徴量の表に、images → table)、doe_morris(効くつまみを少ない回数でふるう(Morris)、入力なし → design)、sens_morris(要素効果の μ* と σ、design × signal → table)、doe_saltelli(Sobol’ 指数のための標本行列、入力なし → design)、sens_sobol(Sobol’ の S1・ST(交互作用の分も)と信頼区間、design × signal → table)、mv_confounding(データから決まるパラメータの組(共線性指数)、matrix → table)、mv_residual_pca(残差に残った構造 = モデルに足りないもの、matrix → table)
• 予知保全(振動・軸受): bearing_defect_frequencies(軸受の欠陥周波数、入力なし → table)、envelope_spectrum(包絡スペクトル、signal → table)、spectral_kurtosis(衝撃の帯域、signal → table)、ae_band_power(アコースティック・エミッションの帯域パワー、signal × scalar → table)、cepstrum(ケプストラム、signal → table)、order_spectrum(次数スペクトル、signal → table)、stft(時間周波数、signal → table)、motion_magnify(動画からの振動、video → table)、phase_displacement(位相ベースの変位、video → table)
• 電源・電池(内部構造の CT): fbp_volume(電池 CT の再構成、sinostack → voxel)、vol_wall_thickness(電極・セパレータの厚み、voxel → signal)、vol_label(層のラベル、voxel → labels)、vol_region_props(層の性質、labels → table)、metal_trace_interpolate(金属アーチファクト、sinogram → sinogram)、beam_hardening_correct(カッピング、sinogram → sinogram)

fault / bearing / diagnosis

This cluster covers computational and signal processing methods for detecting and diagnosing mechanical bearing faults through vibration analysis and machine learning.

• 使う op: dc_local_contrast_norm(照明むらを除いた局所コントラスト、image → image)、dc_rpca_sparse(背景から外れる疎な欠陥、image → image)、defect_contrast(照明条件での欠陥コントラスト、table → table)、illumination_design(欠陥が出る照明の設計、入力なし → table)、lighting_sweep(照明を振って最良を探す、入力なし → pairs)、auto_threshold(二値化、image → region)、ほか 22 個: blob_count、circularity、sfp_normals、check_difference、blob_features、separate_touching、pod_hitmiss_fit、varmodel_fit、varmodel_score、anomaly_pro、solid_angle_sweep、solid_angle_render、bearing_defect_frequencies、envelope_spectrum、spectral_kurtosis、ae_band_power、cepstrum、order_spectrum、stft、spc_ewma、motion_magnify、phase_displacement
• 代表論文(新しい順 + いちばん古い 1 本):
  • You Keshun et al. (2024). A novel rolling bearing fault diagnosis method based on time-series fusion transformer with interpretability analysis. 10.1080/10589759.2024.2425813
  • Xin Li et al. (2024). A review on convolutional neural network in rolling bearing fault diagnosis. 10.1088/1361-6501/ad356e
  • Mailson Ribeiro Santos et al. (2024). SHapley Additive exPlanations (SHAP) for Efficient Feature Selection in Rolling Bearing Fault Diagnosis. 10.3390/make6010016
  • Xingjun Zhu et al. (2023). Adaptive Multiscale Convolution Manifold Embedding Networks for Intelligent Fault Diagnosis of Servo Motor-Cylindrical Rolling Bearing Under Variable Working Conditions. 10.1109/tmech.2023.3314215
  • Nguyễn Đức Thuận et al. (2023). HUST bearing: a practical dataset for ball bearing fault diagnosis. 10.1186/s13104-023-06400-4
  • Brian P. Graney et al. (2012). Rolling Element Bearing Analysis. W2181634873

denture / bases / molding

This cluster covers comparative analyses of denture base fabrication methods, specifically examining how injection molding, CAD/CAM milling, compression molding, and rapid prototyping techniques affect the accuracy and mechanical properties of dental prostheses.

• 使う op: dc_local_contrast_norm(照明むらを除いた局所コントラスト、image → image)、dc_rpca_sparse(背景から外れる疎な欠陥、image → image)、defect_contrast(照明条件での欠陥コントラスト、table → table)、illumination_design(欠陥が出る照明の設計、入力なし → table)、lighting_sweep(照明を振って最良を探す、入力なし → pairs)、auto_threshold(二値化、image → region)、ほか 12 個: blob_count、circularity、sfp_normals、check_difference、blob_features、separate_touching、pod_hitmiss_fit、varmodel_fit、varmodel_score、anomaly_pro、solid_angle_sweep、solid_angle_render
• 代表論文(新しい順 + いちばん古い 1 本):
  • RutikaC Patankar et al. (2022). Comparative evaluation of flexural strength of denture base resin materials processed using compression molding technique, injection molding technique, and computer-aided design CAM technique: An in vitro study. 10.4103/1735-3327.361360
  • Suji Lee et al. (2019). Comparing accuracy of denture bases fabricated by injection molding, CAD/CAM milling, and rapid prototyping method. 10.4047/jap.2019.11.1.55

molding / injection / process

This cluster covers injection molding as a manufacturing process, with emphasis on computer modeling, process optimization, quality control, and monitoring techniques.

• 使う op: dc_local_contrast_norm(照明むらを除いた局所コントラスト、image → image)、dc_rpca_sparse(背景から外れる疎な欠陥、image → image)、defect_contrast(照明条件での欠陥コントラスト、table → table)、illumination_design(欠陥が出る照明の設計、入力なし → table)、lighting_sweep(照明を振って最良を探す、入力なし → pairs)、auto_threshold(二値化、image → region)、ほか 12 個: blob_count、circularity、sfp_normals、check_difference、blob_features、separate_touching、pod_hitmiss_fit、varmodel_fit、varmodel_score、anomaly_pro、solid_angle_sweep、solid_angle_render
• 代表論文(新しい順 + いちばん古い 1 本):
  • Mateusz Czepiel et al. (2023). Advanced Injection Molding Methods: Review. 10.3390/ma16175802
  • Jinsu Gim et al. (2022). A review of current advancements in high surface quality injection molding: Measurement, influencing factors, prediction, and control. 10.1016/j.polymertesting.2022.107718
  • Chi-Hung Lo et al. (2022). Design of Injection Molding of Side Mirror Cover. 10.18494/sam3828
  • Senthil Kumaran Selvaraj et al. (2022). A Review on Machine Learning Models in Injection Molding Machines. 10.1155/2022/1949061
  • Donggyun Im et al. (2021). A Data-Centric Approach to Design and Analysis of a Surface-Inspection System Based on Deep Learning in the Plastic Injection Molding Industry. 10.3390/pr9111895
  • Donald F. Heaney (2012). Handbook of metal injection molding. 10.1533/9780857096234

cooling / molding / injection

This cluster covers the design and optimization of cooling systems in injection molding processes, with emphasis on conformal cooling channels and their impact on part quality and production efficiency.

• 使う op: dc_local_contrast_norm(照明むらを除いた局所コントラスト、image → image)、dc_rpca_sparse(背景から外れる疎な欠陥、image → image)、defect_contrast(照明条件での欠陥コントラスト、table → table)、illumination_design(欠陥が出る照明の設計、入力なし → table)、lighting_sweep(照明を振って最良を探す、入力なし → pairs)、auto_threshold(二値化、image → region)、ほか 18 個: blob_count、circularity、sfp_normals、check_difference、blob_features、separate_touching、pod_hitmiss_fit、varmodel_fit、varmodel_score、anomaly_pro、solid_angle_sweep、solid_angle_render、fbp_volume、vol_wall_thickness、vol_label、vol_region_props、metal_trace_interpolate、beam_hardening_correct
• 代表論文(新しい順 + いちばん古い 1 本):
  • Yuandi Wang et al. (2023). Design and Optimization of Conformal Cooling Channels for Increasing Cooling Efficiency in Injection Molding. 10.3390/app13137437
  • Van–Thuc Nguyen et al. (2023). Conformal Cooling Channel Design for Improving Temperature Distribution on the Cavity Surface in the Injection Molding Process. 10.3390/polym15132793
  • Yuan-Ping Luh et al. (2023). Automated Design of Honeycomb Conformal Cooling Channels for Improving Injection Molding Quality. 10.30657/pea.2023.29.7
  • Sofia B. Rocha et al. (2022). Optimization to Assist Design and Analysis of Temperature Control Strategies for Injection Molding—A Review. 10.3390/ma15124048
  • Andreas Kirchheim et al. (2021). Dynamic conformal cooling improves injection molding. 10.1007/s00170-021-06794-0
  • Alban Agazzi et al. (2013). Uniform Cooling and Part Warpage Reduction in Injection Molding Thanks to the Design of an Effective Cooling System. 10.4028/www.scientific.net/kem.554-557.1611

injection / molding / molded

This cluster covers injection molding and related manufacturing techniques, including process optimization, material properties, and emerging applications in microfluidics and metal powder molding.

• 使う op: dc_local_contrast_norm(照明むらを除いた局所コントラスト、image → image)、dc_rpca_sparse(背景から外れる疎な欠陥、image → image)、defect_contrast(照明条件での欠陥コントラスト、table → table)、illumination_design(欠陥が出る照明の設計、入力なし → table)、lighting_sweep(照明を振って最良を探す、入力なし → pairs)、auto_threshold(二値化、image → region)、ほか 12 個: blob_count、circularity、sfp_normals、check_difference、blob_features、separate_touching、pod_hitmiss_fit、varmodel_fit、varmodel_score、anomaly_pro、solid_angle_sweep、solid_angle_render
• 代表論文(新しい順 + いちばん古い 1 本):
  • Elham Sharifi et al. (2021). Assessing the Suitability of Freeform Injection Molding for Low Volume Injection Molded Parts: A Design Science Approach. 10.3390/su13031313
  • Po-Hsu Allen Chen et al. (2018). Sequential design of an injection molding process using a calibrated predictor. 10.1080/00224065.2018.1474696
  • Ulri N. Lee et al. (2018). Fundamentals of rapid injection molding for microfluidic cell-based assays. 10.1039/c7lc01052d
  • Manoraj Mohan et al. (2016). Review on the Effects of Process Parameters on Strength, Shrinkage, and Warpage of Injection Molding Plastic Component. 10.1080/03602559.2015.1132466
  • Daniele Annicchiarico et al. (2014). Review of Factors that Affect Shrinkage of Molded Part in Injection Molding. 10.1080/10426914.2014.880467
  • Randall M. German (2013). Progress in Titanium Metal Powder Injection Molding. 10.3390/ma6083641

molding / injection / taguchi

This cluster covers the application of Taguchi design methodology and injection molding process optimization to control critical quality parameters like shrinkage, warpage, and dimensional accuracy.

• 使う op: dc_local_contrast_norm(照明むらを除いた局所コントラスト、image → image)、dc_rpca_sparse(背景から外れる疎な欠陥、image → image)、defect_contrast(照明条件での欠陥コントラスト、table → table)、illumination_design(欠陥が出る照明の設計、入力なし → table)、lighting_sweep(照明を振って最良を探す、入力なし → pairs)、auto_threshold(二値化、image → region)、ほか 37 個: blob_count、circularity、sfp_normals、check_difference、blob_features、separate_touching、pod_hitmiss_fit、varmodel_fit、varmodel_score、anomaly_pro、solid_angle_sweep、solid_angle_render、spc_xbar_r、spc_cusum、spc_ewma、spc_capability、spc_hotelling_t2、null_distribution、evidence_quantile、doe_orthogonal_array、doe_fractional_factorial、doe_central_composite、doe_box_behnken、doe_lhs、doe_encode、doe_decode、doe_main_effects、rsm_fit、mv_pls、mv_pca、feat_image_vector、doe_morris、sens_morris、doe_saltelli、sens_sobol、mv_confounding、mv_residual_pca
• 代表論文(新しい順 + いちばん古い 1 本):
  • Lei Zhang et al. (2025). Analysis and optimization of injection molding process on warpage based on Taguchi design and PSO algorithm. 10.1007/s00170-025-15099-5
  • Xuemei Li et al. (2024). The influence of mold design and process parameters on dimensional shrinkage of perfluoroalkoxy alkane injection molding parts. 10.1002/app.55757
  • Jinping Chen et al. (2023). Design and Parametric Optimization of the Injection Molding Process Using Statistical Analysis and Numerical Simulation. 10.3390/pr11020414
  • Melsiani Rosdiani Fillipin Saduk et al. (2022). Engineering Design of Soft Gripper and Manufacturing by TPE Hollow Injection Molding. 10.1109/iet-iceta56553.2022.9971524
  • S. Ramesh et al. (2021). Simulation Process of Injection Molding and Optimization for Automobile Instrument Parameter in Embedded System. 10.1155/2021/9720297
  • Shaik Mohamed Mohamed Yusoff et al. (2012). A Plastic Injection Molding Process Characterization Using Experimental Design Technique: A Case Study. 10.11113/jt.v41.686

molding / injection / mold

This cluster covers the technical fundamentals of injection molding and related manufacturing processes, including process modeling, optimization, and material behavior across different scales—from macroscopic polymer components to micro/nano-scale precision manufacturing.

• 使う op: dc_local_contrast_norm(照明むらを除いた局所コントラスト、image → image)、dc_rpca_sparse(背景から外れる疎な欠陥、image → image)、defect_contrast(照明条件での欠陥コントラスト、table → table)、illumination_design(欠陥が出る照明の設計、入力なし → table)、lighting_sweep(照明を振って最良を探す、入力なし → pairs)、auto_threshold(二値化、image → region)、ほか 12 個: blob_count、circularity、sfp_normals、check_difference、blob_features、separate_touching、pod_hitmiss_fit、varmodel_fit、varmodel_score、anomaly_pro、solid_angle_sweep、solid_angle_render
• 代表論文(新しい順 + いちばん古い 1 本):
  • Matthew R. Bont et al. (2021). A review of liquid silicone rubber injection molding: Process variables and process modeling. 10.1002/pen.25618
  • Jože Tavčar et al. (2020). A multicriteria function for polymer gear design optimization. 10.1093/jcde/qwaa097
  • Kun‐Cheng Ke et al. (2020). Quality Prediction for Injection Molding by Using a Multilayer Perceptron Neural Network. 10.3390/polym12081812
  • Jinchuan Zhao et al. (2020). Lightweight and tough PP/talc composite foam with bimodal nanoporous structure achieved by microcellular injection molding. 10.1016/j.matdes.2020.109051
  • Dario Loaldi et al. (2020). Experimental Validation of Injection Molding Simulations of 3D Microparts and Microstructured Components Using Virtual Design of Experiments and Multi-Scale Modeling. 10.3390/mi11060614
  • Nan Zhang et al. (2012). Towards nano-injection molding. 10.1016/s1369-7021(12)70092-5

clamping / injection / molding

This cluster covers the technical and design aspects of injection molding machines, with emphasis on clamping mechanisms, soft robotics manufacturing applications, and machine ergonomics.

• 使う op: dc_local_contrast_norm(照明むらを除いた局所コントラスト、image → image)、dc_rpca_sparse(背景から外れる疎な欠陥、image → image)、defect_contrast(照明条件での欠陥コントラスト、table → table)、illumination_design(欠陥が出る照明の設計、入力なし → table)、lighting_sweep(照明を振って最良を探す、入力なし → pairs)、auto_threshold(二値化、image → region)、ほか 12 個: blob_count、circularity、sfp_normals、check_difference、blob_features、separate_touching、pod_hitmiss_fit、varmodel_fit、varmodel_score、anomaly_pro、solid_angle_sweep、solid_angle_render
• 代表論文(新しい順 + いちばん古い 1 本):
  • Michael A. Bell et al. (2021). Injection Molding of Soft Robots. 10.1002/admt.202100605
  • Long–Chang Hsieh et al. (2021). Optimal design on Watt-chain double-toggle mold clamping mechanism for injection molding machine. 10.1177/00368504211041488
  • Ming‐Shyan Huang et al. (2012). Kansei Engineering Applied to the Form Design of Injection Molding Machines. 10.4236/ojapps.2012.23030

materials / review / design

811 本

This cluster encompasses systematic reviews and syntheses of intervention designs, material properties, and environmental processes. It covers evidence-based evaluations of health interventions (obesity prevention, medical treatments), educational and behavioral design approaches, and physical system analyses (vegetation-climate interactions, cloud microphysics modeling).

• Intervention review methodology: Systematic synthesis of clinical trial evidence to assess effectiveness and safety across diverse health domains (nutrition, rehabilitation, acupuncture, parenteral nutrition)
• Design evaluation frameworks: Structured approaches to comparing intervention protocols, devices, and implementation methods for measurable outcomes
• Behavioral and educational design: Evidence-based strategies for modifying practices (complementary feeding, workplace ergonomics, preventive care)
• Multi-scale physical modeling: Comparative analysis of representation methods (bulk parameterization vs. spectral approaches) in complex systems
• Occupational and environmental health design: Work-break scheduling, ergonomic interventions, and exposure reduction strategies

使う op(テーマ → op の対応表から。ノートで型と例を確かめてから使う):

• 材料(見え方・組織・表面): spectrum_to_srgb(分光 → 色、signal → vector)、thin_film_reflectance(薄膜の干渉色、signal → signal)、material_catalog(材質の外観表、入力なし → table)、oren_nayar(粗い面の反射、normalmap → image2d)、brdf_microfacet(微小面の反射、normalmap → image2d)、metallic_flake_normals(メタリック塗装、入力なし → normalmap)、corrosion_mask(腐食域、入力なし → image2d)、cooc_feature_matrix(組織のテクスチャ、image → feature)、gabor(方向性のテクスチャ、image → image)、vol_granulometry(CT の空隙分布、voxel → measurement)、entropy_image(組織の乱れ、image → image)、material_table(材料の光学物性表(83 種、光学系と観察対象が同じ表を引く)、入力なし → table)、material_optical_constants(波長ごとの n, k、入力なし → table)、material_transmittance(厚さ d の透過率、入力なし → pairs)、thermo_optic_coefficient(温度で変わる屈折率、入力なし → table)、phase_fractions(粉末 X 線回折から相の割合、signal × signal × table → table)、scherrer_size(回折線の幅から結晶子の大きさ(Scherrer)、scalar × scalar × scalar → scalar)
• 文献のメタ議論(教育・レビュー) —— op なし(Fullseye の外)
• トポロジー最適化・格子構造の評価 —— 入口: 進化エンジン(docs/ENGINE.md、docs/EVOLUTION_ENVIRONMENT.md)は設計パラメータを適応度で回す側
• トポロジー最適化・格子構造の評価: vol_euler_number(連結性(閉じた空隙の数)、voxel → measurement)、vol_wall_thickness(最小肉厚、voxel → signal)、edt_jfa(距離変換(肉厚・隙間)、voxel → sdf)、mesh_volume(体積(材料量)、mesh → measurement)、inertia_tensor(慣性テンソル、points → matrix)、principal_moments(主慣性モーメント、points → descriptor)
• 板金・成形(曲げ角・ひずみ): strain_from_displacement(DIC の変位 → ひずみ、image2d × image2d → image2d)、piv_deform_pass(変形を追う相関、image2d × image2d × flow2d → flow2d)、correlation_quality(相関の信頼度、image2d × image2d × flow2d → image2d)、speckle_quality(スペックルの良さ、image2d → table)、angle_between_planes(曲げ角、primitive → measurement)、ransac_plane(フランジ面、points → primitive)、fit_spline_curve(曲げ線の当てはめ、points → points)、curvature_torsion(曲線の曲率、points → pairs)
• SPC(管理図・工程能力): spc_xbar_r(X̄-R 管理図、matrix → table)、spc_cusum(小さなずれの累積検出、signal → table)、spc_ewma(指数平滑の管理図、signal → table)、spc_capability(Cp / Cpk、signal → table)、spc_hotelling_t2(多変量の管理、matrix → table)、null_distribution(帰無分布、signal → table)、evidence_quantile(証拠量の位置、measurement × table → table)

cells / immune / disease

This cluster encompasses research on disease prevention, management, and treatment across multiple biological systems, with emphasis on interventional strategies, molecular mechanisms, and therapeutic approaches.

• 代表論文(新しい順 + いちばん古い 1 本):
  • Yongjie Li et al. (2025). Immune cell regulatory networks in chronic obstructive pulmonary disease: mechanistic analysis from innate to adaptive immunity. 10.3389/fimmu.2025.1651808
  • Abdul Raheem et al. (2025). The emerging role of tissue regulatory T cells in tissue repair and regeneration. 10.3389/fimmu.2025.1640113
  • Jussi Saukkonen et al. (2024). Updates on the Treatment of Drug-Susceptible and Drug-Resistant Tuberculosis: An Official ATS/CDC/ERS/IDSA Clinical Practice Guideline. 10.1164/rccm.202410-2096st
  • Maryam Ehsasatvatan et al. (2024). Designing and immunomolecular analysis of a new broad-spectrum multiepitope vaccine against divergent human papillomavirus types. 10.1371/journal.pone.0311351
  • Klaudia Jomová et al. (2024). Heavy metals: toxicity and human health effects. 10.1007/s00204-024-03903-2
  • Fiona Duncan et al. (2012). Fatigue after Stroke: A Systematic Review of Associations with Impaired Physical Fitness. 10.1111/j.1747-4949.2011.00741.x

generative / learning / intelligence

This cluster explores generative AI and machine learning systems that autonomously create, optimize, and learn from data.

• 使う op: vol_euler_number(連結性(閉じた空隙の数)、voxel → measurement)、vol_wall_thickness(最小肉厚、voxel → signal)、edt_jfa(距離変換(肉厚・隙間)、voxel → sdf)、vol_granulometry(空隙の大きさ分布、voxel → measurement)、mesh_volume(体積(材料量)、mesh → measurement)、inertia_tensor(慣性テンソル、points → matrix)、ほか 1 個: principal_moments
• 代表論文(新しい順 + いちばん古い 1 本):
  • Sanghee Lee et al. (2025). The influence of generative AI with prompt engineering on creative design in architectural education. 10.1080/13467581.2025.2552446
  • Daniel Lee et al. (2025). Prompt engineering in higher education: a systematic review to help inform curricula. 10.1186/s41239-025-00503-7
  • Harpreet Kaur Channi et al. (2025). AI‐Driven Generative Design Redefines the Engineering Process. 10.1002/9781394271078.ch17
  • Tao Shen et al. (2024). Accurate RNA 3D structure prediction using a language model-based deep learning approach. 10.1038/s41592-024-02487-0
  • Reihaneh Samsami (2024). Optimizing the Utilization of Generative Artificial Intelligence (AI) in the AEC Industry: ChatGPT Prompt Engineering and Design. 10.3390/civileng5040049
  • Steve A. Maas et al. (2012). FEBio: Finite Elements for Biomechanics. 10.1115/1.4005694

metal / organic / materials

This cluster covers the intersection of metallic and organic materials science, with particular emphasis on advanced electrocatalysts, battery technologies, and metal-organic frameworks.

• 使う op: spectrum_to_srgb(分光 → 色、signal → vector)、thin_film_reflectance(薄膜の干渉色、signal → signal)、material_catalog(材質の外観表、入力なし → table)、oren_nayar(粗い面の反射、normalmap → image2d)、brdf_microfacet(微小面の反射、normalmap → image2d)、metallic_flake_normals(メタリック塗装、入力なし → normalmap)、ほか 11 個: corrosion_mask、cooc_feature_matrix、gabor、vol_granulometry、entropy_image、material_table、material_optical_constants、material_transmittance、thermo_optic_coefficient、phase_fractions、scherrer_size
• 代表論文(新しい順 + いちばん古い 1 本):
  • Amarajothi Dhakshinamoorthy et al. (2024). Metal–organic framework heterojunctions for photocatalysis. 10.1039/d3cs00205e
  • Li‐Yuan Zhu et al. (2023). Advances in Noble Metal-Decorated Metal Oxide Nanomaterials for Chemiresistive Gas Sensors: Overview. 10.1007/s40820-023-01047-z
  • Zhuo Li et al. (2023). Ionic Conduction in Polymer‐Based Solid Electrolytes. 10.1002/advs.202201718
  • Henry Adenusi et al. (2023). Lithium Batteries and the Solid Electrolyte Interphase (SEI)—Progress and Outlook. 10.1002/aenm.202203307
  • Qianwen Liu et al. (2021). A Review on Metal- and Metal Oxide-Based Nanozymes: Properties, Mechanisms, and Applications. 10.1007/s40820-021-00674-8
  • Antonin Faes et al. (2012). A Review of RedOx Cycling of Solid Oxide Fuel Cells Anode. 10.3390/membranes2030585

materials / applications / properties

This cluster encompasses the design, processing, characterization, and practical applications of advanced materials across diverse domains including intermetallics, metallic glasses, biomimetic scaffolds, and functional materials.

• 使う op: spectrum_to_srgb(分光 → 色、signal → vector)、thin_film_reflectance(薄膜の干渉色、signal → signal)、material_catalog(材質の外観表、入力なし → table)、oren_nayar(粗い面の反射、normalmap → image2d)、brdf_microfacet(微小面の反射、normalmap → image2d)、metallic_flake_normals(メタリック塗装、入力なし → normalmap)、ほか 11 個: corrosion_mask、cooc_feature_matrix、gabor、vol_granulometry、entropy_image、material_table、material_optical_constants、material_transmittance、thermo_optic_coefficient、phase_fractions、scherrer_size
• 代表論文(新しい順 + いちばん古い 1 本):
  • N. G. McCrum et al. (2026). Principles of Polymer Engineering. 10.1093/9780191821677.001.0001
  • Claudio Zeni et al. (2025). A generative model for inorganic materials design. 10.1038/s41586-025-08628-5
  • Adam B. Peters et al. (2024). Materials design for hypersonics. 10.1038/s41467-024-46753-3
  • Zilong Han et al. (2024). Design of Fatigue‐Resistant Hydrogels. 10.1002/adfm.202313498
  • Doksoo Lee et al. (2023). Data‐Driven Design for Metamaterials and Multiscale Systems: A Review. 10.1002/adma.202305254
  • Brian Evans (2012). Practical 3D Printers. 10.1007/978-1-4302-4393-9

climate / atmospheric / variability

This cluster encompasses climate modeling, atmospheric processes, and variability research, focusing on how Earth system models represent physical processes like cloud microphysics, evapotranspiration, and ocean circulation.

• 使う op: spc_xbar_r(X̄-R 管理図、matrix → table)、spc_cusum(小さなずれの累積検出、signal → table)、spc_ewma(指数平滑の管理図、signal → table)、spc_capability(Cp / Cpk、signal → table)、spc_hotelling_t2(多変量の管理、matrix → table)、null_distribution(帰無分布、signal → table)、ほか 1 個: evidence_quantile
• 代表論文(新しい順 + いちばん古い 1 本):
  • The Core Writing Team et al. (2024). Climate Change 2023 Synthesis Report. 10.1163/9789004322714_cclc_2023-0258-1193
  • Katherine Calvin et al. (2023). IPCC, 2023: Climate Change 2023: Synthesis Report. Contribution of Working Groups I, II and III to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Core Writing Team, H. Lee and J. Romero (eds.)]. IPCC, Geneva, Switzerland. 10.59327/ipcc/ar6-9789291691647
  • Vivek K. Arora et al. (2020). Carbon–concentration and carbon–climate feedbacks in CMIP6 models and their comparison to CMIP5 models. 10.5194/bg-17-4173-2020
  • Neil C. Swart et al. (2019). The Canadian Earth System Model version 5 (CanESM5.0.3). 10.5194/gmd-12-4823-2019
  • Isaac M. Held et al. (2019). Structure and Performance of GFDL’s CM4.0 Climate Model. 10.1029/2019ms001829
  • Daniel Stone et al. (2012). Tropospheric OH and HO2 radicals: field measurements and model comparisons. 10.1039/c2cs35140d

forming / design / sheet

This cluster covers parametric design methodologies, optimization techniques, and multi-criteria decision-making frameworks applied across engineering disciplines.

• 使う op: strain_from_displacement(DIC の変位 → ひずみ、image2d × image2d → image2d)、piv_deform_pass(変形を追う相関、image2d × image2d × flow2d → flow2d)、correlation_quality(相関の信頼度、image2d × image2d × flow2d → image2d)、speckle_quality(スペックルの良さ、image2d → table)、angle_between_planes(曲げ角、primitive → measurement)、ransac_plane(フランジ面、points → primitive)、ほか 2 個: fit_spline_curve、curvature_torsion
• 代表論文(新しい順 + いちばん古い 1 本):
  • Priyanca Ford et al. (2026). The Aegis Forward-Base Burner: Q_E Closure, Plug Requirement, and Mirror Confinement in an Islanded Low-Neutron D–³He Tandem Mirror. 10.5281/zenodo.22645693
  • Ullah et al. (2026). Applied Mechanics and Materials. 10.4028/www.scientific.net/amm
  • Orlando Corigliano et al. (2022). On the Technology of Solid Oxide Fuel Cell (SOFC) Energy Systems for Stationary Power Generation: A Review. 10.3390/su142215276
  • Samya Sen et al. (2022). Thixotropic spectra and Ashby-style charts for thixotropy. 10.1122/8.0000446
  • Qinying Yan et al. (2022). The Finite Element Analysis Research on Microneedle Design Strategy and Transdermal Drug Delivery System. 10.3390/pharmaceutics14081625
  • Soonhung Han et al. (2012). Hydrodynamic hull form optimization using parametric models. 10.1007/s00773-011-0148-8

dfma / design / assembly

This cluster covers Design for Manufacturing and Assembly (DFMA) methodologies and their integration into product development processes.

• 代表論文(新しい順 + いちばん古い 1 本):
  • Chathuri Widanage et al. (2024). Integrating Design for Manufacture and Assembly (DfMA) with BIM for infrastructure. 10.1016/j.autcon.2024.105705
  • Sadaf Montazeri et al. (2024). Design for Manufacturing and Assembly (DfMA) in Construction: A Holistic Review of Current Trends and Future Directions. 10.3390/buildings14010285
  • Zbigniew Humienny et al. (2023). ISO GPS and ASME GDT standards – differences and similarities in definitions of measurands. 10.24425/mms.2023.147954
  • Muhammad Naeem et al. (2023). A Step-by-Step Process of Thematic Analysis to Develop a Conceptual Model in Qualitative Research. 10.1177/16094069231205789
  • Anda Iviana Juniani et al. (2022). Design for Manufacturing, Assembly, and Reliability: An Integrated Framework for Product Redesign and Innovation. 10.3390/designs6050088
  • Ibuchim Ogunkah et al. (2012). Investigating Factors Affecting Material Selection: The Impacts on Green Vernacular Building Materials in the Design-Decision Making Process. 10.3390/buildings2010001

industry / digital / manufacturing

This cluster covers the intersection of digital transformation, automation, and manufacturing systems in Industry 4.0 environments.

• 代表論文(新しい順 + いちばん古い 1 本):
  • Marcello M. Mariani et al. (2024). Generative artificial intelligence in innovation management: A preview of future research developments. 10.1016/j.jbusres.2024.114542
  • Godfred Fobiri et al. (2022). Reality Capture in Construction Project Management: A Review of Opportunities and Challenges. 10.3390/buildings12091381
  • Max Schwenzer et al. (2021). Review on model predictive control: an engineering perspective. 10.1007/s00170-021-07682-3
  • Jianxin Jiao et al. (2021). Design Engineering in the Age of Industry 4.0. 10.1115/1.4051041
  • Deborah Sumter et al. (2021). Key Competencies for Design in a Circular Economy: Exploring Gaps in Design Knowledge and Skills for a Circular Economy. 10.3390/su13020776
  • Jeannette Pols (2012). Care at a Distance : On the Closeness of Technology. 10.26530/oapen_413032

op → クラスタ(逆引き)

この文献層で「使う op」に挙がった op と、それが出たクラスタ(上位 › 葉)。op から「どの知識の文脈で使うか」を引く。

op	出たクラスタ
acoustic_layer_stack	cad / parametric / design
ae_band_power	injection / molding / fault
angle_between_planes	materials / review / design
angular_resample	optimization / topology / design; robot / robotic / control
angular_spectrum_propagate	cad / parametric / design
anomaly_pro	injection / molding / fault
area_center	additive / manufacturing / printing
auto_threshold	injection / molding / fault
band_snr	optimization / topology / design; robot / robotic / control
beam_hardening_correct	injection / molding / fault
beamform_delay_sum	cad / parametric / design
bearing_defect_frequencies	injection / molding / fault
blob_count	additive / manufacturing / printing; injection / molding / fault
blob_features	injection / molding / fault
box_sdf	point / shape / cloud; cad / parametric / design
brdf_microfacet	additive / manufacturing / printing; materials / review / design
canny	fatigue / bolted / joints
capsule_sdf	point / shape / cloud; cad / parametric / design
cepstrum	injection / molding / fault
chamfer_distance	point / shape / cloud
check_difference	injection / molding / fault
circularity	injection / molding / fault
comminution_law_fit	additive / manufacturing / printing
contact_radius_fit	robot / robotic / control
contours_to_gcode	additive / manufacturing / printing
conv_gci	cad / parametric / design; fatigue / bolted / joints
conv_observed_order	cad / parametric / design; fatigue / bolted / joints
cooc_feature_matrix	additive / manufacturing / printing; materials / review / design
correlation_quality	materials / review / design
corrosion_mask	additive / manufacturing / printing; materials / review / design
curvature_torsion	materials / review / design
cylinder_sdf	point / shape / cloud; cad / parametric / design
cylinder_unwrap	fatigue / bolted / joints; optimization / topology / design; robot / robotic / control
d2_distribution	point / shape / cloud
dc_local_contrast_norm	injection / molding / fault
dc_rpca_sparse	injection / molding / fault
decimate_qem	point / shape / cloud
defect_contrast	injection / molding / fault
dem_slope	robot / robotic / control
displacement_series	fatigue / bolted / joints; optimization / topology / design; robot / robotic / control
distance_ridge	fatigue / bolted / joints
dlt_pose	robot / robotic / control
doe_box_behnken	optimization / topology / design; injection / molding / fault
doe_central_composite	optimization / topology / design; injection / molding / fault
doe_decode	optimization / topology / design; injection / molding / fault
doe_encode	optimization / topology / design; injection / molding / fault
doe_fractional_factorial	optimization / topology / design; injection / molding / fault
doe_lhs	optimization / topology / design; injection / molding / fault
doe_main_effects	optimization / topology / design; injection / molding / fault
doe_morris	optimization / topology / design; injection / molding / fault
doe_orthogonal_array	optimization / topology / design; injection / molding / fault
doe_saltelli	optimization / topology / design; injection / molding / fault
echo_phase_polarity	cad / parametric / design
edges_sub_pix	fatigue / bolted / joints; optimization / topology / design; robot / robotic / control
edt_jfa	optimization / topology / design; additive / manufacturing / printing; materials / review / design
entropy_image	additive / manufacturing / printing; materials / review / design
envelope_spectrum	fatigue / bolted / joints; optimization / topology / design; robot / robotic / control; injection / molding / fault
estimate_flow	robot / robotic / control
estimate_normals	point / shape / cloud
evidence_quantile	injection / molding / fault; materials / review / design
fbp_volume	injection / molding / fault
feat_image_vector	optimization / topology / design; injection / molding / fault
fit_bspline_surface	point / shape / cloud; cad / parametric / design
fit_cone	point / shape / cloud
fit_rigid	robot / robotic / control
fit_spline_curve	materials / review / design
fit_superquadric	point / shape / cloud; cad / parametric / design
fmcw_range_profile	cad / parametric / design
focus_sweep_height_video	additive / manufacturing / printing
gabor	additive / manufacturing / printing; materials / review / design
gcc_delay	cad / parametric / design
gcode_extrusion_volume	additive / manufacturing / printing
gcode_layer_image	additive / manufacturing / printing
gcode_read	additive / manufacturing / printing
gcode_time_estimate	additive / manufacturing / printing
gcode_write	additive / manufacturing / printing
gradient_lut_invert	robot / robotic / control
grid_coords	point / shape / cloud; cad / parametric / design
hausdorff_distance	point / shape / cloud
hertz_force	robot / robotic / control
hessian3d	fatigue / bolted / joints
hole_centre_from_rgbd	robot / robotic / control
hopper_discharge_rate	additive / manufacturing / printing
icp_point2plane	point / shape / cloud; robot / robotic / control
illumination_design	injection / molding / fault
inertia_tensor	optimization / topology / design; additive / manufacturing / printing; materials / review / design
insertion_failure_classify	robot / robotic / control
iterate_fixed_point	cad / parametric / design; fatigue / bolted / joints
iv_richardson_lucy_until	cad / parametric / design; fatigue / bolted / joints
jamming_diagram	robot / robotic / control
large_deformation_inverse	robot / robotic / control
lighting_sweep	injection / molding / fault
m1_measure_pairs	fatigue / bolted / joints; optimization / topology / design; robot / robotic / control
marker_track	robot / robotic / control
matched_filter_snr	cad / parametric / design
material_catalog	additive / manufacturing / printing; materials / review / design
material_optical_constants	additive / manufacturing / printing; materials / review / design
material_table	additive / manufacturing / printing; materials / review / design
material_transmittance	additive / manufacturing / printing; materials / review / design
mc_photon_slab	cad / parametric / design; fatigue / bolted / joints
mc_sequential_mean	cad / parametric / design; fatigue / bolted / joints
membrane_shear_field	robot / robotic / control
mesh_area	point / shape / cloud
mesh_isotropic_remesh	point / shape / cloud
mesh_sample_points	point / shape / cloud
mesh_slice_contours	additive / manufacturing / printing
mesh_slice_stack	additive / manufacturing / printing
mesh_to_voxel	point / shape / cloud
mesh_volume	point / shape / cloud; optimization / topology / design; additive / manufacturing / printing; materials / review / design
metal_trace_interpolate	injection / molding / fault
metallic_flake_normals	additive / manufacturing / printing; materials / review / design
mindlin_partial_slip	robot / robotic / control
motion_magnify	fatigue / bolted / joints; additive / manufacturing / printing; injection / molding / fault
music_doa	cad / parametric / design
mv_confounding	optimization / topology / design; injection / molding / fault
mv_pca	optimization / topology / design; injection / molding / fault
mv_pls	optimization / topology / design; injection / molding / fault
mv_residual_pca	optimization / topology / design; injection / molding / fault
null_distribution	injection / molding / fault; materials / review / design
optimize_pose_graph	robot / robotic / control
order_spectrum	fatigue / bolted / joints; optimization / topology / design; robot / robotic / control; injection / molding / fault
oren_nayar	additive / manufacturing / printing; materials / review / design
particle_image_d50	additive / manufacturing / printing
particle_size_dx	additive / manufacturing / printing
peg_offset_from_rgbd	robot / robotic / control
phase_displacement	optimization / topology / design; robot / robotic / control; injection / molding / fault
phase_fractions	additive / manufacturing / printing; materials / review / design
piv_deform_pass	materials / review / design
piv_multipass	fatigue / bolted / joints; optimization / topology / design; robot / robotic / control
plane_sdf	point / shape / cloud; cad / parametric / design
pnp_ransac	robot / robotic / control
pod_hitmiss_fit	injection / molding / fault
poisson_lite	point / shape / cloud
polar_unwrap	fatigue / bolted / joints; optimization / topology / design; robot / robotic / control
powder_flowability_class	additive / manufacturing / printing
principal_moments	optimization / topology / design; additive / manufacturing / printing; materials / review / design
print_layer_defect_map	additive / manufacturing / printing
procrustes_distance	point / shape / cloud
pulse_echo_ascan	cad / parametric / design
range_doppler_map	cad / parametric / design
ransac_cylinder	point / shape / cloud
ransac_plane	point / shape / cloud; materials / review / design
read_3mf	additive / manufacturing / printing
register_cpd_rigid	robot / robotic / control
register_fpfh	point / shape / cloud
relative_pose	robot / robotic / control
repose_angle_heightmap	additive / manufacturing / printing
reprojection_error	robot / robotic / control
riesz_displacement_series	optimization / topology / design; robot / robotic / control
rsm_fit	optimization / topology / design; injection / molding / fault
scherrer_size	additive / manufacturing / printing; materials / review / design
sdf_intersect	point / shape / cloud; cad / parametric / design
sdf_offset	point / shape / cloud; cad / parametric / design
sdf_smooth_union	point / shape / cloud; cad / parametric / design
sdf_subtract	point / shape / cloud; cad / parametric / design
sdf_to_occupancy	point / shape / cloud; cad / parametric / design
sdf_union	point / shape / cloud; cad / parametric / design
segment_rigid_motions	fatigue / bolted / joints; optimization / topology / design; robot / robotic / control
sens_morris	optimization / topology / design; injection / molding / fault
sens_sobol	optimization / topology / design; injection / molding / fault
separate_touching	injection / molding / fault
sfp_normals	injection / molding / fault
shape_index	point / shape / cloud
shape_pca	point / shape / cloud
simcal_discrepancy	cad / parametric / design
simcal_fit	cad / parametric / design
simcal_identifiability	cad / parametric / design
simcal_model	cad / parametric / design
simcal_observe	cad / parametric / design
simcal_report	cad / parametric / design
simcal_screen	cad / parametric / design
simcal_simulate	cad / parametric / design
simcal_uncertainty	cad / parametric / design
simcal_validate	cad / parametric / design
slip_predict	robot / robotic / control
solid_angle_render	injection / molding / fault
solid_angle_sweep	injection / molding / fault
spc_capability	injection / molding / fault; materials / review / design
spc_cusum	injection / molding / fault; materials / review / design
spc_ewma	injection / molding / fault; materials / review / design
spc_hotelling_t2	injection / molding / fault; materials / review / design
spc_xbar_r	injection / molding / fault; materials / review / design
speckle_quality	materials / review / design
spectral_kurtosis	injection / molding / fault
spectrum_to_srgb	additive / manufacturing / printing; materials / review / design
spiral_search_points	robot / robotic / control
statistical_outlier_removal	point / shape / cloud
stft	cad / parametric / design; injection / molding / fault
strain_from_displacement	fatigue / bolted / joints; materials / review / design
tac_contact_mask	robot / robotic / control
tac_height_from_shading	robot / robotic / control
tac_pressure_proxy	robot / robotic / control
tac_shear_field	robot / robotic / control
tac_surface_normal	robot / robotic / control
temporal_band_power	optimization / topology / design; robot / robotic / control
temporal_bandpass	additive / manufacturing / printing
thermo_optic_coefficient	additive / manufacturing / printing; materials / review / design
thin_film_reflectance	additive / manufacturing / printing; materials / review / design
torque_decompose	robot / robotic / control
varmodel_fit	injection / molding / fault
varmodel_score	injection / molding / fault
vertex_curvature	point / shape / cloud
video_spacetime_cube	additive / manufacturing / printing
video_summary_keyframes	additive / manufacturing / printing
vol_euler_number	optimization / topology / design; additive / manufacturing / printing; materials / review / design
vol_frangi	fatigue / bolted / joints
vol_granulometry	optimization / topology / design; additive / manufacturing / printing; materials / review / design
vol_label	injection / molding / fault
vol_region_props	injection / molding / fault
vol_wall_thickness	optimization / topology / design; additive / manufacturing / printing; injection / molding / fault; materials / review / design
volseq_magnify_motion	additive / manufacturing / printing
voxel_grid_downsample	point / shape / cloud
write_3mf	additive / manufacturing / printing

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