AM Surface Defect Point Cloud Dataset

Description

The AM Surface Defect Point Cloud Dataset provides on-machine laser-scanned point cloud data for monitoring and correcting surface deviations in additive manufacturing (AM) parts. This dataset is a valuable resource for researchers and engineers working on in-process quality control, surface defect identification, and adaptive repair strategies in metal AM.

This dataset is associated with the AM Surface Defect repository on GitHub:
🔗 GitHub Repository

It also relates to the following publication:
📄 Surface Defect Identification and Adaptive Correction in AM

The dataset contains several representative point cloud samples captured from real-world manufactured AM parts. These data samples facilitate the development of advanced algorithms for surface defect detection and adaptive correction.

Key Features

• On-Machine Laser Scanning: Point cloud data collected directly from real AM components.
• Surface Deviation Detection: Captures dimensional deviations and surface defects.
• Adaptive Repair Strategy: Supports automatic toolpath generation for defect correction.
• High-Fidelity 3D Point Cloud Data: Enables precise defect analysis and in-situ monitoring.
• Compatible with Point Cloud Processing Tools: Usable in Open3D, PCL, and machine learning frameworks.

Usage

This dataset can be used for:

• Point cloud-based defect detection and classification
• Dimensional deviation analysis for AM quality control
• AI-driven surface defect identification and adaptive correction
• Toolpath generation for AM repair processes
• Training deep learning models for point cloud processing

Citation

If you use this dataset in your research, please cite:

@dataset{chen2024am_pointcloud,
  author    = {Chen, Lequn},
  title     = {AM Surface Defect Point Cloud Dataset},
  year      = {2024},
  publisher = {Zenodo},
  url       = {https://zenodo.org/record/[Dataset-ID]}
}