期刊
ADDITIVE MANUFACTURING
卷 30, 期 -, 页码 -出版社
ELSEVIER
DOI: 10.1016/j.addma.2019.100916
关键词
Directed energy deposition; Lack-of-fusion defects; In-process optical emission spectroscopy; Plume imaging; Kronecker graph product; Sensor data fusion
资金
- Office of Naval Research [N00014-11-1-0668]
- Air Force Research Laboratory through America Makes [FA8650-12-2-7230]
- National Science Foundation [CMMI-1719388, CMMI-1739696, CMMI-1752069]
The objective of this work is to detect in situ the occurrence of lack-of-fusion defects in titanium alloy (Ti-6Al-4V) parts made using directed energy deposition (DED) additive manufacturing (AM). We use data from two types of in-process sensors, namely, a spectrometer and an optical camera which are integrated into an Optomec MR-7 DED machine. Both sensors are focused on capturing the dynamic phenomena around the melt pool region. To detect lack-of-fusion defects, we fuse (combine) the data from the in-process sensors invoking the concept of Kronecker product of graphs. Subsequently, we use the features derived from the graph Kronecker product as inputs to a machine learning algorithm to predict the severity (class or level) of average length of lack-of-fusion defects within a layer, which is obtained from offline X-ray computed tomography of the test parts. We demonstrate that the severity of lack-of-fusion defects is classified with statistical fidelity (F-score) close to 85% for a two-level classification scenario, and approximately 70% for a three-level classification scenario. Accordingly, this work demonstrates the use of heterogeneous in-process sensing and online data analytics for in situ detection of defects in DED metal AM process.
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