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Detecting voids in 3D printing using melt pool time series data

Mahato, Vivek orcid logoORCID: 0000-0001-5697-2536, Ahmed Obeidi, Muhannad orcid logoORCID: 0000-0003-2733-3828, Brabazon, Dermot orcid logoORCID: 0000-0003-3214-6381 and Cunningham, Padraig orcid logoORCID: 0000-0002-3499-0810 (2020) Detecting voids in 3D printing using melt pool time series data. Journal of Intelligent Manufacturing, 33 . pp. 845-852. ISSN 0956-5515

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.
Metadata
Item Type:Article (Published)
Refereed:Yes
Uncontrolled Keywords:Process monitoring; Classification; Time-series
Subjects:Engineering > Materials
Engineering > Mechanical engineering
Engineering > Production engineering
DCU Faculties and Centres:DCU Faculties and Schools > Faculty of Engineering and Computing > School of Mechanical and Manufacturing Engineering
Research Institutes and Centres > Advanced Processing Technology Research Centre (APT)
Research Institutes and Centres > I-Form
Publisher:Springer26099
Official URL:https://dx.doi.org/10.1007/s10845-020-01694-8
Copyright Information:© 2020 Springer
Funders:Science Foundation Ireland (SFI) under Grant Number 16/RC/3872 and is co-funded under the European Regional Development Fund.
ID Code:26099
Deposited On:10 Aug 2021 15:30 by Dermot Brabazon . Last Modified 07 Mar 2022 13:38
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