Automatised quality assessment in additive layer manufacturing using layer-by-layer surface measurements and deep learning


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:
Divisions: College of Engineering & Physical Sciences > School of Engineering and Technology > Mechanical, Biomedical & Design
Additional Information: © 2021 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license ( 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
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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 (ORCID Profile 0000-0001-7261-3832)
Ji, Ze
Kerfriden, Pierre
Gage, Daniel
Feyer, Felix
Körner, Carolin
Bigot, Samuel

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