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High-speed Interpretable Representation Learning of Melt Pool Signatures in Metal Additive Manufacturing

 
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cris.virtual.orcid0000-0001-8346-131X
cris.virtual.orcid0000-0001-9228-4810
cris.virtual.orcid0000-0003-4456-4353
cris.virtualsource.department1496e7b2-0515-4a16-9401-c80e1c9541b6
cris.virtualsource.department920e6d7b-82d4-4c4f-a5ea-2a639fef1b5d
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dc.contributor.authorVandecasteele, Mathieu
dc.contributor.authorVerhees, Dries
dc.contributor.authorPhilips, Wilfried
dc.contributor.authorBey-Temsamani, Abdellatif
dc.contributor.authorBooth, Brian
dc.contributor.imecauthorVandecasteele, Mathieu
dc.contributor.imecauthorPhilips, Wilfried
dc.contributor.imecauthorBooth, Brian G.
dc.contributor.orcidimecVandecasteele, Mathieu::0000-0001-8346-131X
dc.contributor.orcidimecPhilips, Wilfried::0000-0003-4456-4353
dc.date.accessioned2025-03-06T20:46:07Z
dc.date.available2025-03-06T20:46:07Z
dc.date.issued2024
dc.description.wosFundingTextThis work was financially supported by the imec ICON VLAIO project 'MultipLICITY' (HBC.2022.0094), a collaboration between TELIN-IPI-imec, Ghent University; Flanders MAKE@KU Leuven; Materialise; imec Vision Lab, University of Antwerp; Flanders MAKE; Dekimo; Manufacturing Processes and Systems (MaPS); Xenics; Rejig; and AdditiveLab. This research was partially supported by Flanders Make, the strategic research centre for the manufacturing industry. This research received funding from the Flemish Government under the 'Onderzoeksprogramma Artificiele Intelligentie (AI) Vlaanderen' programme. We wish to thank all the partners for their assistance in technical discussions and data collection.
dc.identifier.doi10.1109/CASE59546.2024.10711429
dc.identifier.eisbn979-8-3503-5851-3
dc.identifier.isbn979-8-3503-5852-0
dc.identifier.issn2161-8070
dc.identifier.urihttps://imec-publications.be/handle/20.500.12860/45331
dc.publisherIEEE
dc.source.beginpage1618
dc.source.conferenceIEEE 20th International Conference on Automation Science and Engineering (CASE)
dc.source.conferencedate2024-08-28
dc.source.conferencelocationBari
dc.source.endpage1624
dc.source.numberofpages7
dc.title

High-speed Interpretable Representation Learning of Melt Pool Signatures in Metal Additive Manufacturing

dc.typeProceedings paper
dspace.entity.typePublication
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