BenDFM: A taxonomy and synthetic CAD dataset for manufacturability assessment in sheet metal bending

• URL: http://arxiv.org/abs/2603.13102v1
• Date: Fri, 13 Mar 2026 15:57:44 GMT
• Title: BenDFM: A taxonomy and synthetic CAD dataset for manufacturability assessment in sheet metal bending
• Abstract summary: 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.
• Score: 0.5156484100374058
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Predicting the manufacturability of CAD designs early, in terms of both feasibility and required effort, is a key goal of Design for Manufacturing (DFM). Despite advances in deep learning for CAD and its widespread use in manufacturing process selection, learning-based approaches for predicting manufacturability within a specific process remain limited. Two key challenges limit progress: inconsistency across prior work in how manufacturability is defined and consequently in the associated learning targets, and a scarcity of suitable datasets. Existing labels vary significantly: they may reflect intrinsic design constraints or depend on specific manufacturing capabilities (such as available tools), and they range from discrete feasibility checks to continuous complexity measures. Furthermore, industrial datasets typically contain only manufacturable parts, offering little signal for infeasible cases, while existing synthetic datasets focus on simple geometries and subtractive processes. To address these gaps, we propose a taxonomy of manufacturability metrics along the axes of configuration dependence and measurement type, allowing clearer scoping of generalizability and learning objectives. Next, we introduce BenDFM, the first synthetic dataset for manufacturability assessment in sheet metal bending. BenDFM contains 20,000 parts, both manufacturable and unmanufacturable, generated with process-aware bending simulations, providing both folded and unfolded geometries and multiple manufacturability labels across the taxonomy, enabling systematic study of previously unexplored learning-based DFM challenges. We benchmark two state-of-the-art 3D learning architectures on BenDFM, showing that graph-based representations that capture relationships between part surfaces achieve better accuracy, and that predicting metrics that depend on specific manufacturing setups remains more challenging.

Related papers

• CAD-feature enhanced machine learning for manufacturing effort estimation on sheet metal bending parts [0.3250512744763586]
  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.
  arXiv  Detail & Related papers  (2026-05-12T15:30:12Z)
• FORGE: Fine-grained Multimodal Evaluation for Manufacturing Scenarios [58.34124792457706]
  The manufacturing sector is increasingly adopting Multimodal Large Language Models (MLLMs) to transition from simple perception to autonomous execution.<n>Progress is hindered by data scarcity and a lack of fine-grained domain semantics in existing datasets.<n>We first construct a high-quality dataset that combines realworld 2D images and 3D point clouds, annotated with fine-grained domain semantics.<n>We then evaluate 18 state-of-the-art MLLMs across three manufacturing tasks, namely verification, structural surface inspection, and assembly verification, revealing significant performance gaps.
  arXiv  Detail & Related papers  (2026-04-08T12:23:27Z)
• 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)
• LLMs for Analog Circuit Design Continuum (ACDC) [10.15990970510281]
  Large Language Models (LLMs) and transformer architectures have shown impressive reasoning and generation capabilities.<n>This work focuses on AI-assisted design where humans remain in the loop.<n>Our results highlight key reliability challenges, including sensitivity to data format, instability in generated designs, and limited generalization to unseen circuit configurations.
  arXiv  Detail & Related papers  (2025-12-09T23:57:28Z)
• Mitra: Mixed Synthetic Priors for Enhancing Tabular Foundation Models [85.64873567417396]
  We introduce Mitra, a TFM trained on a curated mixture of synthetic priors selected for their diversity, distinctiveness, and performance on real-world data.<n>Mitra consistently outperforms state-of-the-art TFMs, such as TabPFNv2 and TabICL, across both classification and regression benchmarks.
  arXiv  Detail & Related papers  (2025-10-24T07:15:06Z)
• Large Language Model Sourcing: A Survey [84.63438376832471]
  Large language models (LLMs) have revolutionized artificial intelligence, shifting from supporting objective tasks to empowering subjective decision-making.<n>Due to the black-box nature of LLMs and the human-like quality of their generated content, issues such as hallucinations, bias, unfairness, and copyright infringement become significant.<n>This survey presents a systematic investigation into provenance tracking for content generated by LLMs, organized around four interrelated dimensions.
  arXiv  Detail & Related papers  (2025-10-11T10:52:30Z)
• PB-IAD: Utilizing multimodal foundation models for semantic industrial anomaly detection in dynamic manufacturing environments [0.0]
  This paper presents PB-IAD (Prompt-based Industrial Anomaly Detection), a novel framework for anomaly detection.<n>It addresses three key requirements of dynamic production environments: data sparsity, agile adaptability, and domain user centricity.<n>It is benchmarked to state-of-the-art methods for anomaly detection such as PatchCore.
  arXiv  Detail & Related papers  (2025-08-20T07:53:13Z)
• 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)
• Meta-Statistical Learning: Supervised Learning of Statistical Inference [59.463430294611626]
  This work demonstrates that the tools and principles driving the success of large language models (LLMs) can be repurposed to tackle distribution-level tasks.<n>We propose meta-statistical learning, a framework inspired by multi-instance learning that reformulates statistical inference tasks as supervised learning problems.
  arXiv  Detail & Related papers  (2025-02-17T18:04:39Z)
• 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)
• DA-VEGAN: Differentiably Augmenting VAE-GAN for microstructure reconstruction from extremely small data sets [110.60233593474796]
  DA-VEGAN is a model with two central innovations. A $beta$-variational autoencoder is incorporated into a hybrid GAN architecture. A custom differentiable data augmentation scheme is developed specifically for this architecture.
  arXiv  Detail & Related papers  (2023-02-17T08:49:09Z)

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.