MiCLAD as a platform for real-time monitoring and machine learning in laser metal deposition

Research output: Chapter in Book/Report/Conference proceedingConference paper

13 Citations (Scopus)

Abstract

The MiCLAD machine designed at the VUB, Belgium, allows for closed-loop controlled laser metal deposition including various in-situ optical based measurement systems. These integrated sensors collect information on deposition geometry and temperature during the building process. Hence, each cubic millimeter of material that is either added or removed is mapped to its digital twin with a millisecond temporal resolution in the machines database. This paper introduces the platform and its capabilities by focusing on the procedure of obtaining the necessary training data for the future application of machine learning algorithms, with the goal of controlling the geometry and temperature history during additive manufacturing.
Original languageEnglish
Title of host publication11th CIRP Conference on Photonic Technologies [LANE 2020]
Place of PublicationErlangen
PublisherElsevier
Pages456-461
Number of pages6
Volume94
DOIs
Publication statusPublished - 16 Sep 2020
EventLANE 2020: 11th CIRP Conference on Photonic Technologies - Virtual edition, Erlangen, Germany
Duration: 7 Sep 202010 Oct 2020
https://www.lane-conference.org/

Publication series

NameProcedia CIRP
PublisherElsevier
ISSN (Print)2212-8271

Conference

ConferenceLANE 2020
CountryGermany
CityErlangen
Period7/09/2010/10/20
Internet address

Bibliographical note

Funding Information:
The authors would like to acknowledge Mr. Wim Devesse and Mr. Jorge Sanchez Medina for their contributions in setting up the camera integration in the machine. As well as Mr. Jona Gladines from the University of Antwerp for delivery of the CAD geometry of the test plate. This work was financed by the Research Foundation Flanders (FO)W through project HiPAS and the Department of Economics, Innovation and Science (EIW ) of the Flanders government, Belgium, through the project HyLaFORM.

Funding Information:
The authors would like to acknowledge Mr. Wim Devesse and Mr. Jorge Sanchez Medina for their contributions in setting up the camera integration in the machine. As well as Mr. Jona Gladines from the University of Antwerp for delivery of the CAD geometry of the test plate. This work was financed by the Research Foundation Flanders (FWO) through project HiPAS and the Department of Economics, Innovation and Science (EWI) of the Flanders government, Belgium, through the project HyLaFORM.

Publisher Copyright:
© 2020 The Authors. Published by Elsevier B.V.

Copyright:
Copyright 2022 Elsevier B.V., All rights reserved.

Keywords

  • Laser Metal Deposition
  • Feed-back control
  • Real-time monitoring
  • Machine learning

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