Roux, Léopold Le, Liu, Chao, Ji, Ze, Kerfriden, Pierre, Gage, Daniel, Feyer, Felix, Körner, Carolin and Bigot, Samuel (2021). Automatised quality assessment in additive layer manufacturing using layer-by-layer surface measurements and deep learning. Procedia CIRP, 99 , pp. 342-347.
Abstract
Additive manufacturing (AM) has gained high research interests in the past but comes with some drawbacks, such as the difficulty to do in-situ quality monitoring. In this paper, deep learning is used on electron-optical images taken during the Electron Beam Melting (EBM) process to classify the quality of AM layers to achieve automatized quality assessment. A comparative study of several mainstream Convolutional Neural Networks to classify the images has been conducted. The classification accuracy is up to 95 %, which demonstrates the great potential to support in-process layer quality control of EBM.And the error analysis has shown that some human misclassification were correctly classified by the Convolutional Neural Networks.
Publication DOI: | https://doi.org/10.1016/j.procir.2021.03.050 |
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Divisions: | College of Engineering & Physical Sciences > School of Engineering and Technology > Mechanical, Biomedical & Design |
Funding Information: | This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement n°820774. |
Additional Information: | © 2021 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0) Peer-review under responsibility of the scientific committee of the 14th CIRP Conference on Intelligent Computation in Manufacturing Engineering 15-17 July 2020 Funding Information: This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement n°820774. |
Uncontrolled Keywords: | Additive Manufacturing,Artificial Intelligence,Image recognition,Quality control,Transfert learning,Control and Systems Engineering,Industrial and Manufacturing Engineering |
Publication ISSN: | 2212-8271 |
Last Modified: | 13 Aug 2024 07:04 |
Date Deposited: | 16 Mar 2022 10:38 |
Full Text Link: | |
Related URLs: |
http://www.scop ... tnerID=8YFLogxK
(Scopus URL) https://www.sci ... 3267?via%3Dihub (Publisher URL) |
PURE Output Type: | Conference article |
Published Date: | 2021 |
Published Online Date: | 2021-05-03 |
Accepted Date: | 2020-07-01 |
Authors: |
Roux, Léopold Le
Liu, Chao ( ![]() Ji, Ze Kerfriden, Pierre Gage, Daniel Feyer, Felix Körner, Carolin Bigot, Samuel |
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