Roux LL, Liu C, Ji Z, Kerfriden P, Gage D, Feyer F, Körner C, Bigot S (2021)
Publication Language: English
Publication Type: Conference contribution
Publication year: 2021
Publisher: Elsevier B.V.
Book Volume: 99
Pages Range: 342-347
Conference Proceedings Title: Procedia CIRP
Event location: Naples, ITA
DOI: 10.1016/j.procir.2021.03.050
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.
APA:
Roux, L.L., Liu, C., Ji, Z., Kerfriden, P., Gage, D., Feyer, F.,... Bigot, S. (2021). Automatised quality assessment in additive layer manufacturing using layer-by-layer surface measurements and deep learning. In Roberto Teti, Doriana M. D'Addona (Eds.), Procedia CIRP (pp. 342-347). Naples, ITA: Elsevier B.V..
MLA:
Roux, Léopold Le, et al. "Automatised quality assessment in additive layer manufacturing using layer-by-layer surface measurements and deep learning." Proceedings of the 14th CIRP Conference on Intelligent Computation in Manufacturing Engineering, CIRP ICME 2020, Naples, ITA Ed. Roberto Teti, Doriana M. D'Addona, Elsevier B.V., 2021. 342-347.
BibTeX: Download