Convolutional neural network-based classification for improving the surface quality of metal additive manufactured components
Convolutional neural network-based classification for improving the surface quality of metal additive manufactured components
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DOI：
10.1007/s00170-023-11388-z
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发表时间：
2023-04-11
影响因子：
3.4
通讯作者：
Ahmed, Afzaal
中科院分区：
文献类型：
--
作者：
Abhilash, P. M.;Ahmed, Afzaal
金属添加剂制造(AM)工艺已证明其能够以最小的损耗生产复杂的近净形状产品。然而，由于其表面质量较差，大多数应用都需要对AM制造的部件进行后处理。提出了一种将卷积神经网络(CNN)分类和电火花辅助后处理相结合的方法来提高AMED部件的表面质量。根据表面分类确定抛光深度和抛光次数。通过比较发现，低能量条件下的抛光效果优于高能条件下的抛光效果，表面光洁度显著提高了74%。此外，较低的能量抛光减少了短路放电和元素迁移的发生。对模型进行了5次交叉验证，结果表明，CNN模型对地表状况的预测准确率为96%。此外，建议的方法将表面光洁度从97.3微米大幅提高到12.62微米。
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.