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Quality control and quality assurance are challenges in Direct Metal LaserMelting (DMLM). Intermittent machine diagnostics and downstream partinspections catch problems after undue cost has been incurred processingdefective parts. In this paper we demonstrate two methodologies for in-processfault detection and part quality prediction that can be readily deployed onexisting commercial DMLM systems with minimal hardware modification. Novelfeatures were derived from the time series of common photodiode sensors alongwith standard machine control signals. A Bayesian approach attributesmeasurements to one of multiple process states and a least squares regressionmodel predicts severity of certain material defects.

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