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

• URL: http://arxiv.org/abs/2112.01921v1
• Date: Fri, 3 Dec 2021 14:05:31 GMT
• Title: In situ process quality monitoring and defect detection for direct metal laser melting
• Authors: Sarah Felix, Saikat Ray Majumder, H. Kirk Mathews, Michael Lexa, Gabriel Lipsa, Xiaohu Ping, Subhrajit Roychowdhury, Thomas Spears
• Abstract summary: In-process fault detection and part quality prediction can be readily deployed on existing commercial DMLM systems. A Bayesian approach attributes measurements to one of multiple process states and a least squares regression model predicts severity of certain material defects.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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 can be readily deployed on 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. A Bayesian approach attributes measurements to one of multiple process states and a least squares regression model predicts severity of certain material defects.

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