In situ process quality monitoring and defect detection for direct metal laser melting

Quality control and quality assurance are challenges in direct metal laser melting (DMLM). Intermittent machine diagnostics and downstream part inspections catch problems after undue cost has been incurred processing defective parts. In this paper we demonstrate two methodologies for in-process faul...

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Bibliographic Details
Published in:Scientific reports 2022-05, Vol.12 (1), p.8503-8503, Article 8503
Main Authors: Felix, Sarah, Ray Majumder, Saikat, Mathews, H. Kirk, Lexa, Michael, Lipsa, Gabriel, Ping, Xiaohu, Roychowdhury, Subhrajit, Spears, Thomas
Format: Article
Language:English
Subjects:
639/166
639/301/930
639/705
639/766/1130
639/766/930
Bayesian analysis
Data acquisition systems
Humanities and Social Sciences
Hypotheses
Lasers
Melting
Monitoring systems
multidisciplinary
OEM
Quality assurance
Quality control
Science
Science (multidisciplinary)
Sensors
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Summary:Quality control and quality assurance are challenges in direct metal laser melting (DMLM). Intermittent machine diagnostics and downstream part inspections catch problems after undue cost has been incurred processing defective parts. In this paper we demonstrate two methodologies for in-process fault detection and part quality prediction that leverage existing commercial DMLM systems with minimal hardware modification. Novel features were derived from the time series of common photodiode sensors along with standard machine control signals. In one methodology, a Bayesian approach attributes measurements to one of multiple process states as a means of classifying process deviations. In a second approach, a least squares regression model predicts severity of certain material defects.
ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-022-12381-4