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

Vivek Mahato, Muhannad Ahmed Obeidi, Dermot Brabazon () and Pádraig Cunningham ()
Additional contact information
Vivek Mahato: University College Dublin
Muhannad Ahmed Obeidi: Dublin City University
Dermot Brabazon: Dublin City University
Pádraig Cunningham: University College Dublin

Journal of Intelligent Manufacturing, 2022, vol. 33, issue 3, No 14, 845-852

Abstract: 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.

Keywords: Process monitoring; Classification; Time-series (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://link.springer.com/10.1007/s10845-020-01694-8 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:joinma:v:33:y:2022:i:3:d:10.1007_s10845-020-01694-8

Ordering information: This journal article can be ordered from
http://www.springer.com/journal/10845

DOI: 10.1007/s10845-020-01694-8

Access Statistics for this article

Journal of Intelligent Manufacturing is currently edited by Andrew Kusiak

More articles in Journal of Intelligent Manufacturing from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().