发表机构
University of Michigan（密歇根大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究针对智能制造中基于深度学习的零件质量检测数据难题，提出构建MFGNet-Gear合成3D数据集，通过参数化生成齿轮几何形状与缺陷，涵盖多种设计和质量等级，提供标注数据，助力相关检测、学习及基准测试，推动3D计量学发展。
AI 中文摘要
智能制造中的质量控制越来越依赖数据驱动方法，特别是深度学习来自动检测制造零件。3D计量学的进展实现了对尺寸精度、表面质量和形状一致性的精细评估。然而，基于点云的深度学习检测方法需要大量涵盖零件设计和缺陷类型的标注数据，获取成本高且耗时。此外，批量生产中缺陷零件很少，类不平衡会降低模型性能。合成数据生成提供了一种有前景的方法来应对这些挑战。本文介绍了MFGNet-Gear，一个公开可用的合成3D数据集，包含24000对多边形网格和点云，涵盖12种齿轮设计和4个质量等级。齿轮几何形状由参数化计算机辅助设计软件生成，尺寸参数受±0.0254毫米的扰动，缺陷参数从代表缺陷形态的分布中采样。对于每个网格，使用Open3D均匀采样100000个点并存储为N×3坐标文本文件。元数据标签识别齿轮设计和质量等级，支持零件设计分类、几何缺陷检测、表示学习和数据集基准测试。MFGNet-Gear为基于深度学习的3D计量学提供了一个开源数据集，其可重复生成管道可扩展到其他零件设计。
英文摘要
Quality control in smart manufacturing increasingly relies on data-driven methods, particularly deep learning, to automate the inspection of manufactured parts. Recent advances in three-dimensional (3D) metrology have enabled fine-scale assessment of dimensional accuracy, surface quality, and shape conformity. However, deep learning methods for point-cloud-based inspection require large volumes of labeled data covering part designs and defect types, which are costly and time-consuming to obtain. Moreover, defective parts are intrinsically rare in mass production, and the resulting class imbalance can degrade model performance and make rare defect types difficult to detect. Synthetic data generation (SDG) offers a promising approach to address these challenges by producing large, balanced, and fully annotated datasets. Yet, applying SDG to precision components requires representing part geometry and defect morphology parametrically, so that design and quality can be co-varied. This article describes MFGNet-Gear, a publicly available synthetic 3D dataset comprising 24,000 paired polygon meshes and point clouds across 12 gear designs and 4 quality classes, with 500 instances per design-quality combination. Gear geometries are generated with parametric computer-aided design software, with dimensional parameters perturbed by $\pm$0.0254 mm and defect parameters sampled from distributions representing defect morphologies. For each mesh, 100,000 points are uniformly sampled using Open3D and stored as N $\times$ 3 coordinate text files. Metadata labels identify the gear design and quality class, supporting part design classification, geometric defect detection, representation learning, and dataset benchmarking. MFGNet-Gear provides an open-source dataset for deep learning-based 3D metrology, with a reproducible generation pipeline extensible to additional part designs.
