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

Le Roux, Léopold, 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. Presented at: 14th CIRP Conference on Intelligent Computation in Manufacturing Engineering 2020, Naples, Italy, 15-17 July 2020. Elsevier, pp. 342-347. 10.1016/j.procir.2021.03.050

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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.

Item Type: Conference or Workshop Item (Paper)
Date Type: Published Online
Status: Published
Schools: Engineering
Publisher: Elsevier
ISSN: 2212-8271
Date of First Compliant Deposit: 28 May 2021
Date of Acceptance: 31 May 2020
Last Modified: 07 Jun 2021 14:55
URI: http://orca.cardiff.ac.uk/id/eprint/141624

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