In situ process quality monitoring and defect detection for direct metal laser melting
This addresses quality assurance challenges for additive manufacturing, but is incremental as it builds on existing sensor data and methods.
The paper tackled quality control in Direct Metal Laser Melting by developing two in-process fault detection and part quality prediction methods, achieving deployment on existing systems with minimal hardware modifications.
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.