Le Roux, Léopold, Liu, Chao, Ji, Ze ![]() ![]() ![]() ![]() |
![]() |
PDF
- Published Version
Available under License Creative Commons Attribution Non-commercial No Derivatives. Download (976kB) |
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: | Professional Services > Advanced Research Computing @ Cardiff (ARCCA) Schools > Engineering |
Publisher: | Elsevier |
ISSN: | 2212-8271 |
Date of First Compliant Deposit: | 28 May 2021 |
Date of Acceptance: | 31 May 2020 |
Last Modified: | 01 Aug 2024 13:43 |
URI: | https://orca.cardiff.ac.uk/id/eprint/141624 |
Citation Data
Cited 5 times in Scopus. View in Scopus. Powered By Scopus® Data
Actions (repository staff only)
![]() |
Edit Item |