Detecting voids in 3D printing using melt pool time series data
Detecting voids in 3D printing using melt pool time series data
复制标题
使用熔池时间序列数据检测 3D 打印中的空隙
DOI：
10.1007/s10845-020-01694-8
复制
发表时间：
2020
影响因子：
8.3
通讯作者：
P. Cunningham
中科院分区：
文献类型：
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作者：
Vivek Mahato;M. Obeidi;D. Brabazon;P. Cunningham
粉末床熔融 (PBF) 已成为金属增材制造中的重要工艺。然而，PBF对工艺参数很敏感，需要仔细管理以确保生产的零件的高质量。在 PBF 中，使用激光或电子束将粉末熔合到零件上。人们认识到熔池的温度是代表过程健康状况的重要信号。在本文中，使用时间序列数据的机器学习（ML）方法来监测熔池温度以检测异常。与时间序列分类的其他机器学习研究一致，使用动态时间规整和 k 最近邻分类器。所提出的过程可有效检测 PBF 中的空洞。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。
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.