A Synthetic 3D Gear Dataset for Manufacturing Quality Inspection (MFGNet-Gear)

2026-07-11

AI Summary

This study addresses the performance limitations of deep learning models in 3D point cloud quality inspection for smart manufacturing, which stem from scarce real-world defect data, high annotation costs, and severe class imbalance. To overcome these challenges, the authors propose a parameterized CAD-based synthetic data generation framework. They introduce the first joint parametric modeling of gear geometry and four distinct defect types, yielding MFGNet-Gear—a fully annotated, class-balanced dataset comprising 24,000 paired mesh-point cloud samples (each with 100,000 points) across 12 gear designs and four quality grades. The dataset is generated via uniform Open3D sampling and calibrated to reflect realistic defect statistics, thereby supporting tasks such as defect detection and design classification, and establishing a scalable, reproducible benchmark for 3D metrology in deep learning.

Abstract

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.

Problem

• synthetic data generation
• 3D metrology
• quality inspection
• class imbalance
• defect detection

Innovation

• Synthetic Data Generation
• 3D Point Cloud
• Parametric CAD Modeling
• Manufacturing Quality Inspection
• Deep Learning for Metrology