| About this Abstract |
| Meeting |
2023 Annual International Solid Freeform Fabrication Symposium (SFF Symp 2023)
|
| Symposium
|
2023 Annual International Solid Freeform Fabrication Symposium (SFF Symp 2023)
|
| Presentation Title |
Unveiling Melt Pool Defect Signatures with Interpretable Machine Learning |
| Author(s) |
Sebastian Larsen, Paul A. Hooper |
| On-Site Speaker (Planned) |
Sebastian Larsen |
| Abstract Scope |
As machine learning (ML) becomes an essential part of in-situ monitoring, model interpretation is needed to ensure robustness and alignment. In this study, we use interpretable ML methods to better understand melt pool defect predictors captured from co-axial high-speed imaging of the laser powder bed fusion process. The methods explored include a variety of explainable AI tools, such as saliency maps and feature importance metrics, to visualise and quantify melt pool features. Results from three datasets are presented: single tracks, laser defocus, and localised defects. The analysis enables a comparison between high-speed imaging and an equivalent photodiode, with each source of improvement measured. Through this analysis, key descriptors can be better understood, providing a deeper understanding of the melt pool behaviour. |
| Proceedings Inclusion? |
Definite: Post-meeting proceedings |