  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.
