Convolutional neural network–based classification for improving the surface quality of metal additive manufactured components

Abhilash, P. M. and Ahmed, Afzaal (2023) Convolutional neural network–based classification for improving the surface quality of metal additive manufactured components. The International Journal of Advanced Manufacturing Technology, 126 (9-10). pp. 3873-3885. ISSN 1433-3015 (https://doi.org/10.1007/s00170-023-11388-z)

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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.

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