Automatised quality assessment in additive layer manufacturing using layer-by-layer surface measurements and deep learning - Roux, Léopold Le; Liu, Chao; Ji, Ze; Kerfriden, Pierre; Gage, Daniel; Feyer, Felix; Körner, Carolin; Bigot, Samuel (Procedia CIRP, 2021)
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.
CRIS-Team Scopus Importer:2021-06-04
Keywords: Additive Manufacturing; Artificial Intelligence; Image recognition; Quality control; Transfert learning