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.
|Number of pages||6|
|Early online date||3 May 2021|
|Publication status||Published - 2021|
|Event||14th CIRP Conference on Intelligent Computation in Manufacturing Engineering, CIRP ICME 2020 - Naples, Italy|
Duration: 15 Jul 2020 → 17 Jul 2020
Bibliographical note© 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
This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement n°820774.
- Additive Manufacturing
- Artificial Intelligence
- Image recognition
- Quality control
- Transfert learning