CAD-feature enhanced machine learning for manufacturing effort estimation on sheet metal bending parts

• URL: http://arxiv.org/abs/2605.12266v1
• Date: Tue, 12 May 2026 15:30:12 GMT
• Title: CAD-feature enhanced machine learning for manufacturing effort estimation on sheet metal bending parts
• Abstract summary: Graph-based machine learning has emerged as a promising approach for manufacturability analysis by learning directly from CAD models represented as Boundary Representations (B-reps)<n>We propose a hybrid approach that addresses this challenge by enriching B-rep attributed adjacency graphs with manufacturing features recognized through a rule-based module.<n> Experiments on both a large-scale synthetic manufacturability benchmark and a real-world industrial dataset with measured bending times, demonstrate that combining domain knowledge with graph-based learning improves prediction accuracy across both tasks.
• Score: 0.3250512744763586
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Graph-based machine learning has emerged as a promising approach for manufacturability analysis by learning directly from CAD models represented as Boundary Representations (B-reps), exploiting both surface geometry and topological connectivity. However, purely geometric representations often lack the process-specific semantics required for accurate manufacturability prediction: many manufacturing factors, such as surface roles or bend intent, are not explicitly encoded in shape alone and are difficult for data-driven models to infer reliably. We propose a hybrid approach that addresses this challenge by enriching B-rep attributed adjacency graphs with manufacturing features recognized through a rule-based module. Applied to sheet metal bending, recognized features, such as bend characteristics, flange lengths, and surface roles are integrated as node attributes, concentrating the learning signal on process-relevant geometric patterns. Experiments on both a large-scale synthetic manufacturability benchmark and a real-world industrial dataset with measured bending times, one of the first such validations on genuine production data, demonstrate that combining domain knowledge with graph-based learning improves prediction accuracy across both tasks. The results demonstrate that hybrid modeling offers a feasible and effective path toward deployable tools for manufacturability assessment and effort estimation in industrial CAD environments.

Related papers

• BenDFM: A taxonomy and synthetic CAD dataset for manufacturability assessment in sheet metal bending [0.5156484100374058]
  We propose a taxonomy of manufacturability metrics along the axes of configuration dependence and measurement type.<n>We introduce BenDFM, the first synthetic dataset for manufacturability assessment in sheet metal bending.<n>BenDFM contains 20,000 parts, both manufacturable and unmanufacturable, generated with process-aware bending simulations.
  arXiv  Detail & Related papers  (2026-03-13T15:57:44Z)
• The Density of Cross-Persistence Diagrams and Its Applications [67.12329106585666]
  Topological Data Analysis (TDA) provides powerful tools to explore the shape and structure of data through topological features such as clusters, loops, and voids.<n>Persistence diagrams are a cornerstone of TDA, capturing the evolution of these features across scales.<n>Cross-persistence diagrams (cross-barcodes), introduced recently, address this limitation by characterizing relationships between topological features of two point clouds.
  arXiv  Detail & Related papers  (2026-03-12T07:33:15Z)
• Kolmogorov-Arnold Networks-Based Tolerance-Aware Manufacturability Assessment Integrating Design-for-Manufacturing Principles [0.0]
  This study proposes a methodology that evaluates manufacturability directly from parametric design features.<n>The approach employs Kolmogorov-Arnold Networks (KANs) to learn functional relationships between design parameters, tolerances, and manufacturability outcomes.
  arXiv  Detail & Related papers  (2026-01-09T22:12:47Z)
• Nonparametric Data Attribution for Diffusion Models [57.820618036556084]
  Data attribution for generative models seeks to quantify the influence of individual training examples on model outputs.<n>We propose a nonparametric attribution method that operates entirely on data, measuring influence via patch-level similarity between generated and training images.
  arXiv  Detail & Related papers  (2025-10-16T03:37:16Z)
• GUST: Quantifying Free-Form Geometric Uncertainty of Metamaterials Using Small Data [0.0]
  GUST (Generative Uncertainty learning via Self-supervised pretraining and Transfer learning) is a framework for quantifying free-form geometric uncertainties inherent in the manufacturing of metamaterials.
  arXiv  Detail & Related papers  (2025-05-28T18:40:15Z)
• Explainable Prediction of the Mechanical Properties of Composites with CNNs [18.157728232751474]
  We show that convolutional neural networks (CNNs) equipped with methods from explainable AI (XAI) can be successfully deployed to solve this problem.<n>Our approach uses customised CNNs trained on a dataset we generate using transverse tension tests to predict composites' mechanical properties.<n>We then use SHAP and Integrated Gradients, two post-hoc XAI methods, to explain the predictions, showing that the CNNs use the critical geometrical features that influence the composites' behaviour.
  arXiv  Detail & Related papers  (2025-05-20T08:54:06Z)
• GeoBench: Benchmarking and Analyzing Monocular Geometry Estimation Models [41.76935689355034]
  Discriminative and generative pretraining have yielded geometry estimation models with strong generalization capabilities. We build fair and strong baselines for evaluating and analyzing the geometry estimation models. We evaluate monocular geometry estimators on more challenging benchmarks for geometry estimation task with diverse scenes and high-quality annotations.
  arXiv  Detail & Related papers  (2024-06-18T14:44:12Z)
• Relational Learning in Pre-Trained Models: A Theory from Hypergraph Recovery Perspective [60.64922606733441]
  We introduce a mathematical model that formalizes relational learning as hypergraph recovery to study pre-training of Foundation Models (FMs) In our framework, the world is represented as a hypergraph, with data abstracted as random samples from hyperedges. We theoretically examine the feasibility of a Pre-Trained Model (PTM) to recover this hypergraph and analyze the data efficiency in a minimax near-optimal style.
  arXiv  Detail & Related papers  (2024-06-17T06:20:39Z)
• PairCFR: Enhancing Model Training on Paired Counterfactually Augmented Data through Contrastive Learning [49.60634126342945]
  Counterfactually Augmented Data (CAD) involves creating new data samples by applying minimal yet sufficient modifications to flip the label of existing data samples to other classes. Recent research reveals that training with CAD may lead models to overly focus on modified features while ignoring other important contextual information. We employ contrastive learning to promote global feature alignment in addition to learning counterfactual clues.
  arXiv  Detail & Related papers  (2024-06-09T07:29:55Z)
• Geometric Deep Learning for Structure-Based Drug Design: A Survey [83.87489798671155]
  Structure-based drug design (SBDD) leverages the three-dimensional geometry of proteins to identify potential drug candidates. Recent advancements in geometric deep learning, which effectively integrate and process 3D geometric data, have significantly propelled the field forward.
  arXiv  Detail & Related papers  (2023-06-20T14:21:58Z)
• GELATO: Geometrically Enriched Latent Model for Offline Reinforcement Learning [54.291331971813364]
  offline reinforcement learning approaches can be divided into proximal and uncertainty-aware methods. In this work, we demonstrate the benefit of combining the two in a latent variational model. Our proposed metrics measure both the quality of out of distribution samples as well as the discrepancy of examples in the data.
  arXiv  Detail & Related papers  (2021-02-22T19:42:40Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.