RESUMO
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.
RESUMO
Ventricular tachycardia (V-tach) is a very serious condition that occurs when the ventricles are driven at high rates. The abnormal excitation pathways make ventricular contraction less synchronous resulting in less effective filling and emptying of the left ventricles. However, almost half of the V-tach alarms declared through processing of patterns observed in electrocardiography are not clinically actionable. The focus of this study is to provide guidance on determining whether a technically-correct V-tach alarm is clinically-actionable by determining its "hemodynamic impact". A supervisory learning approach based on conditional inference trees to determine the hemodynamic impact of a V-tach alarm based on extracted features is described. According to preliminary results on a subset of Multiparameter intelligent monitoring in intensive care II (MIMIC-II) database, true positive rate of more than 90% can be achieved.
