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

P. M. Abhilash, Afzaal Ahmed

Research output: Contribution to journalArticlepeer-review

5 Citations (Scopus)
12 Downloads (Pure)

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 languageEnglish
Pages (from-to)3873-3885
Number of pages13
JournalThe International Journal of Advanced Manufacturing Technology
Volume126
Issue number9-10
Early online date11 Apr 2023
DOIs
Publication statusPublished - 30 Jun 2023

Keywords

  • metal additive manufacturing
  • artificial Intelligence
  • polishing
  • image processing

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