Detecting Voids in 3D Printing Using Melt Pool Time Series Data
|Title:||Detecting Voids in 3D Printing Using Melt Pool Time Series Data||Authors:||Mahato, Vivek; Obeidi, Muhannad Ahmed; Brabazon, Dermot; Cunningham, Pádraig||Permanent link:||http://hdl.handle.net/10197/11740||Date:||22-Oct-2020||Online since:||2020-11-25T17:35:56Z||Abstract:||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.||Funding Details:||European Commission - European Regional Development Fund
Science Foundation Ireland
|Type of material:||Journal Article||Publisher:||Springer||Journal:||Journal of Intelligent Manufacturing||Start page:||1||End page:||9||Copyright (published version):||2020 Springer||Keywords:||Process monitoring; Classification; Time-Series||DOI:||10.1007/s10845-020-01694-8||Language:||en||Status of Item:||Peer reviewed||This item is made available under a Creative Commons License:||https://creativecommons.org/licenses/by-nc-nd/3.0/ie/|
|Appears in Collections:||Computer Science Research Collection|
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