A semi-supervised machine learning approach for in-process monitoring of laser powder bed fusion
Laser powder bed fusion (L-PBF), despite the tremendous potential in metal additive manufacturing, is still facing a significant barrier toward wider adoption due to the current lack of quality assurance. Notable efforts aiming at effective quality control of L-PBF products rely on using machine lea...
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sg-ntu-dr.10356-1647652023-02-13T08:13:35Z A semi-supervised machine learning approach for in-process monitoring of laser powder bed fusion Nguyen, Ngoc Vu Hum, Allen Jun Wee Tran, Tuan School of Mechanical and Aerospace Engineering Singapore Centre for 3D Printing Engineering::Mechanical engineering Machine Learning Additive Manufacturing Laser powder bed fusion (L-PBF), despite the tremendous potential in metal additive manufacturing, is still facing a significant barrier toward wider adoption due to the current lack of quality assurance. Notable efforts aiming at effective quality control of L-PBF products rely on using machine learning (ML) of monitoring data to either identify possible defects or predict the product quality. In this study, we propose a semi-supervised ML approach using layerwise monitoring images. We train the ML model using reference monitoring images to classify surface appearances of samples printed without defect and with a common type of defect in L-PBF, i.e., overheating. The trained ML model enables determination of overheated regions in L-PBF products during printing process. We then demonstrate our ML's capability by performing prediction on a test sample having overhanging structures. National Research Foundation (NRF) This research is supported by NAMIC Singapore and funded by the National Research Foundation Singapore under its Innovation Cluster Programme. 2023-02-13T08:13:35Z 2023-02-13T08:13:35Z 2022 Journal Article Nguyen, N. V., Hum, A. J. W. & Tran, T. (2022). A semi-supervised machine learning approach for in-process monitoring of laser powder bed fusion. Materials Today: Proceedings, 70, 583-586. https://dx.doi.org/10.1016/j.matpr.2022.09.607 2214-7853 https://hdl.handle.net/10356/164765 10.1016/j.matpr.2022.09.607 2-s2.0-85140652474 70 583 586 en Materials Today: Proceedings © 2022 Elsevier Ltd. All rights reserved. |
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Engineering::Mechanical engineering Machine Learning Additive Manufacturing Nguyen, Ngoc Vu Hum, Allen Jun Wee Tran, Tuan A semi-supervised machine learning approach for in-process monitoring of laser powder bed fusion |
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Laser powder bed fusion (L-PBF), despite the tremendous potential in metal additive manufacturing, is still facing a significant barrier toward wider adoption due to the current lack of quality assurance. Notable efforts aiming at effective quality control of L-PBF products rely on using machine learning (ML) of monitoring data to either identify possible defects or predict the product quality. In this study, we propose a semi-supervised ML approach using layerwise monitoring images. We train the ML model using reference monitoring images to classify surface appearances of samples printed without defect and with a common type of defect in L-PBF, i.e., overheating. The trained ML model enables determination of overheated regions in L-PBF products during printing process. We then demonstrate our ML's capability by performing prediction on a test sample having overhanging structures. |
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School of Mechanical and Aerospace Engineering |
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School of Mechanical and Aerospace Engineering Nguyen, Ngoc Vu Hum, Allen Jun Wee Tran, Tuan |
format |
Article |
author |
Nguyen, Ngoc Vu Hum, Allen Jun Wee Tran, Tuan |
author_sort |
Nguyen, Ngoc Vu |
title |
A semi-supervised machine learning approach for in-process monitoring of laser powder bed fusion |
title_short |
A semi-supervised machine learning approach for in-process monitoring of laser powder bed fusion |
title_full |
A semi-supervised machine learning approach for in-process monitoring of laser powder bed fusion |
title_fullStr |
A semi-supervised machine learning approach for in-process monitoring of laser powder bed fusion |
title_full_unstemmed |
A semi-supervised machine learning approach for in-process monitoring of laser powder bed fusion |
title_sort |
semi-supervised machine learning approach for in-process monitoring of laser powder bed fusion |
publishDate |
2023 |
url |
https://hdl.handle.net/10356/164765 |
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1759058789715148800 |