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.

翻译：质量控制和质量保证是直接金属激光熔炼(DMLM)的挑战。在处理有缺陷的部件发生不当成本后,间歇式机器诊断和下游部分检查会遇到问题。在本文件中,我们展示了两种在加工过程中发现故障的方法和部分质量预测方法,这些方法可以随时安装在现有的商用DMLM系统上,而硬件的修改最小。新特点来自普通光迪传感器的时间序列以及标准的机器控制信号。一种巴伊西亚方法将测量结果归因于一个多个进程州,而一种最低方形回归模型预测某些物质缺陷的严重程度。