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

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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.

ORCID iDs

Abhilash, P. M. ORCID logoORCID: https://orcid.org/0000-0001-5655-6196 and Ahmed, Afzaal;
CORE (COnnecting REpositories)