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 language | English |
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Title of host publication | 11th CIRP Conference on Photonic Technologies [LANE 2020] |
Place of Publication | Erlangen |
Publisher | Elsevier |
Pages | 456-461 |
Number of pages | 6 |
Volume | 94 |
DOIs | |
Publication status | Published - 16 Sep 2020 |
Event | LANE 2020: 11th CIRP Conference on Photonic Technologies - Virtual edition, Erlangen, Germany Duration: 7 Sep 2020 → 10 Oct 2020 https://www.lane-conference.org/ |
Publication series
Name | Procedia CIRP |
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Publisher | Elsevier |
ISSN (Print) | 2212-8271 |
Conference
Conference | LANE 2020 |
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Country | Germany |
City | Erlangen |
Period | 7/09/20 → 10/10/20 |
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