Powder Bed Fusion (PBF) has emerged as an important process in the additive manufacture of metals. However, PBF is sensitive to process parameters and careful management is required to ensure the high quality of parts produced. In PBF, a laser or electron beam is used to fuse powder to the part. It is recognised that the temperature of the melt pool is an important signal representing the health of the process. In this paper, Machine Learning (ML) methods on time-series data are used to monitor melt pool temperature to detect anomalies. In line with other ML research on time-series classification, Dynamic Time Warping and k-Nearest Neighbour classifiers are used. The presented process is effective in detecting voids in PBF. A strategy is then proposed to speed up classification time, an important consideration given the volume of data involved.

粉末床熔合（PBF）已成为金属增材制造中的重要过程。但是，PBF对工艺参数很敏感，需要仔细管理以确保所生产零件的高质量。在PBF中，使用激光或电子束将粉末熔化到零件上。公认的是，熔池的温度是代表过程健康的重要信号。在本文中，基于时间序列数据的机器学习（ML）方法用于监视熔池温度以检测异常。与其他机器学习有关时间序列分类，动态时间规整和k-使用最近的邻居分类器。提出的过程可有效检测PBF中的空隙。然后提出了一种加快分类时间的策略，考虑到所涉及的数据量，这是一个重要的考虑因素。