| About this Abstract |
| Meeting |
Additive Manufacturing Benchmarks 2022 (AM-Bench 2022)
|
| Symposium
|
Additive Manufacturing Benchmarks 2022 (AM-Bench 2022)
|
| Presentation Title |
Defect Prediction on the Base of Thermographic Features in Laser Powder Bed Fusion Utilizing Machine Learning Algorithms |
| Author(s) |
Simon Oster, Tina Becker, Philipp Peter Breese, Nils Scheuschner, Christiane Maierhofer, Tobias Fritsch, Gunther Mohr, Simon Johannes Altenburg |
| On-Site Speaker (Planned) |
Simon Oster |
| Abstract Scope |
Avoiding the formation of defects such as keyhole pores is a major challenge for the production of metal parts by Laser Powder Bed Fusion (LPBF). The use of in-situ monitoring by thermographic cameras is a promising approach to detect defects, however the data is hard to analyze by conventional algorithms. Therefore, we investigate the use of Machine Learning (ML) in this study, as it is a suitable tool to model complex processes with many influencing factors. A ML model for defect prediction is created based on features extracted from process thermograms. The porosity information obtained from an x-ray Micro Computed Tomography (µCT) scan is used as reference. Physical characteristics of the keyhole pore formation are incorporated into the model to increase the prediction accuracy. Based on the prediction result, the quality of the input data is inferred and future demands on in-situ monitoring of LPBF processes are derived. |
| Proceedings Inclusion? |
Undecided |