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Abstract
The metal additive manufacturing (AM) process has proven its capability to produce complex, near-net-shape products with minimal wastage. However, due to its poor surface quality, most applications demand the post-processing of AM-built components. This study proposes a method that combines convolutional neural network (CNN) classification followed by electrical discharge-assisted post-processing to improve the surface quality of AMed components. The polishing depth and passes were decided based on the surface classification. Through comparison, polishing under a low-energy regime was found to perform better than the high-energy regimes with a significant improvement of 74% in surface finish. Also, lower energy polishing reduced the occurrences of short-circuit discharges and elemental migration. A 5-fold cross-validation was performed to validate the models, and the results showed that the CNN model predicts the surface condition with 96% accuracy. Also, the proposed approach improved the surface finish substantially from 97.3 to 12.62 μm.
Original language | English |
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Pages (from-to) | 3873-3885 |
Number of pages | 13 |
Journal | The International Journal of Advanced Manufacturing Technology |
Volume | 126 |
Issue number | 9-10 |
Early online date | 11 Apr 2023 |
DOIs | |
Publication status | Published - 30 Jun 2023 |
Keywords
- metal additive manufacturing
- artificial Intelligence
- polishing
- image processing
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Dive into the research topics of 'Convolutional neural network–based classification for improving the surface quality of metal additive manufactured components'. Together they form a unique fingerprint.Projects
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A Multiscale Digital Twin-Driven Smart Manufacturing System for High Value-Added Products
Luo, X. (Principal Investigator), Qin, Y. (Co-investigator) & Ward, M. (Co-investigator)
EPSRC (Engineering and Physical Sciences Research Council)
1/05/20 → 30/04/25
Project: Research