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

Article; Early Access

关键词: POST-PROCESSING OPERATIONS;    RESIDUAL-STRESS;    ROUGHNESS;    FATIGUE;    IMPROVEMENT;    INTEGRITY;    FINISH;    TI-6AL-4V;    PARTS;   

DOI  :  10.1007/s00170-023-11388-z

来源: SCIE

【 摘 要 】

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 mu m.

【 授权许可】

Free