MFGNet-Gear: A Synthetic 3D Dataset for Geometric Defect Detection in Gears
This mirror contains mesh files only (.ply format, ~11 GB).
For the full dataset including point clouds (.txt, ~30 GB), download from:
Deep Blue Data — DOI: 10.7302/qrdj-n812
For code, documentation, and data generation scripts, see the GitHub repository.
Dataset Overview
| Property | Value |
|---|---|
| Total parts | 24,000 |
| Gear designs | 12 (T20–T40 series) |
| Quality classes | 4 (G0, P0, W0, R0) |
| Parts per class | 500 |
| Mesh format | .ply (polygon mesh) — hosted here |
| Point cloud format | .txt (x,y,z comma-separated) — available on Deep Blue Data |
Quality classes:
| Label | Class | Description |
|---|---|---|
G0 | Good / nominal | No defect |
P0 | Pitting | Surface fatigue damage |
W0 | Tooth wear | Material loss due to friction |
R0 | Root breakage | Fracture at tooth root |
Repository Structure
MFGNet-Gear/
└── mesh_ply/ ← PLY polygon mesh files, organized by gear design
File Naming Convention
T20ID10G0_00001.ply → design T20ID10, good part, index 1
T30ID30R0_00412.ply → design T30ID30, tooth root breakage, index 412
Citation
@article{mei2024deep,
title={Deep learning of 3D point clouds for detecting geometric defects in gears},
author={Mei, Ruo-Syuan and Conway, Christopher H and Bimrose, Miles V and King, William P and Shao, Chenhui},
journal={Manufacturing Letters},
volume={41},
pages={1324--1333},
year={2024},
publisher={Elsevier}
}
A dataset descriptor paper is in preparation.