A semi-supervised machine learning approach for in-process monitoring of laser powder bed fusion

• Main Authors: Nguyen, Ngoc Vu, Hum, Allen Jun Wee, Tran, Tuan
• Other Authors: School of Mechanical and Aerospace Engineering
• Format: Article
• Language: English
• Published: 2023
• Subjects: Engineering::Mechanical engineering; Machine Learning; Additive Manufacturing
• Online Access: https://hdl.handle.net/10356/164765
• Institution: Nanyang Technological University

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.