Semi-supervised machine learning of optical in-situ monitoring data for anomaly detection in laser powder bed fusion. Issue 1 (1st January 2023)

• Record Type: Journal Article
• Authors: Nguyen, Ngoc Vu; Hum, Allen Jun Wee; Do, Truong; Tran, Tuan
• Is Part Of: Virtual and physical prototyping. Volume 18:Issue 1(2023)
• Publication Date: 2023-01-01
• Subjects: Machine learning -- additive manufacturing -- laser powder bed fusion -- quality control; Rapid prototyping -- Periodicals; Virtual computer systems -- Periodicals; Prototypes, Engineering -- Periodicals; Production engineering -- Periodicals; Design, Industrial -- Periodicals; 670.285
• DOI: 10.1080/17452759.2022.2129396
• Languages: English
• ISSNs: 1745-2759

Abstract

ABSTRACT: Laser powder bed fusion (L-PBF) is one of the most widely used metal additive manufacturing technology for fabrication of functional and structural components. However, inconsistency in quality and reliability of L-PBF products is still a significant barrier preventing it from wider adoption. Machine learning (ML) of monitoring data offers a unique solution to effectively identify possible defects and predict the quality of L-PBF products. In this work, we introduce a semi-supervised ML approach to detect anomalies that occurred in L-PBF products. We train the ML model to classify surface appearances in the reference monitoring data. We then correlate the classified appearances to post-process characteristics, e.g. surface roughness, morphology, or tensile strength. We demonstrate that the established correlation enables the determination of key appearances indicative of the quality of the printed samples including anomaly-free, lack-of-fusion and overheated. We further validate our ML approach by performing prediction on test samples having various geometries.