Abstract
This article presents the results of improving an artificial neural network (ANN) to predict the tool wear in high-speed dry turning of SKD11 steel. The original ANN was a back-propagation (BPN) model with the Gradient Descent algorithm (GD). In the improved model, so-called ANN-CS, some parameters were optimized by the Cuckoo search algorithm (CS). Both models were trained, validated, and tested with the same experimental machining dataset based on performance indices, such as R2, MSE, RMSE, and MAPE. The results show that the ANN-CS gives higher prediction accuracy in comparison with the BPN. Especially, the improvement is as high as 30% with the MAPE index. This research result has important implications in choosing artificial intelligence network models suitable for the nature and amount of data that is both large and different. Moreover, this result can help researchers have more basis to choose the training model with high accuracy.
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