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]} }