Issue 70

P. Kulkarni et alii, Frattura ed Integrità Strutturale, 70 (2024) 71-90; DOI: 10.3221/IGF-ESIS.70.04

10 neurons. This study concluded that cutting force, surface roughness, and tool life can be predicted accurately using an ANN model with one hidden layer and 10 neurons.

Figure 9: Neural network training performance for cutting force model.

Figure 10: Prediction accuracy of developed neural network model (a) Training, (b) Validation, (c) Test, (d) All data set. Fig. 9 depicts the training performance of an ANN model using one hidden layer with ten neurons. The optimal validation performance was achieved at epoch 5, with a score of 92.89 with a prediction accuracy of 0.9942. The average squared error between objectives and outputs, or mean squared error, is used to assess the effectiveness of neural network training. Lesser values are preferable. The correlation between outputs (predicted values) and goals (inputs) is measured by R values. Figs. 10(a), (b), (c), and (d) illustrate neural network regression graphs for cutting force with R values discovered close to one during model training, validation, and testing, as well as for the complete data set. Regression coefficients close to one demonstrates that the developed neural network models could be accurately applied to forecast cutting force, surface roughness, and tool life of PVD-coated AlTiN carbide tools when turning Inconel 718 using hybrid MWCNT+Al 2 O 3 nanofluid under MQL.

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