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 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.

Automated summary

This paper demonstrates two methodologies for in-process fault detection and part quality prediction in DMLM. These methodologies leverage existing commercial DMLM systems with minimal hardware modification. The first methodology employs a Bayesian approach for classifying process deviations, while the second methodology uses a least squares regression model to predict the severity of material defects.