# Performance Investigation of Coated Carbide Tools in
## 3 Results
### Machine Learning Models
#### 3.1.1 Artificial Neural Network  
A regression-based AI model was developed using an ANN with PyTorch v2.6, where the model was designed to take three input features, pass them through a hidden layer with 64 neurons, and output a single continuous value as illustrated in Figure 5. The dataset was preprocessed using StandardScaler, which standardized the input features to have zero mean and unit variance before feeding them into the network. The ANN architecture consisted of an input layer with three neurons (representing f\({}_{\text{z}}\), V\({}_{\text{c}}\), and a\({}_{\text{e}}\)), a hidden layer with 64 neurons that applied the ReLU (rectified linear unit) activation function, and a final output layer with a single neuron to produce the regression prediction that corresponds to the accumulative tool life of T\({}_{0.15}\). The mean squared error loss (MSELoss) function was utilized to measure the difference between the predicted and actual values, ensuring the model minimized squared errors during training. The Adam optimizer was employed to update the model's weights efficiently, balancing learning speed and convergence stability. Training was performed over 500 epochs, where the model iteratively adjusted its parameters using backpropagation. During each epoch, the optimizer refined the network weights based on the computed loss, gradually improving the accuracy of predictions. The final trained model was capable of capturing complex relationships within data, making it well-suited for regression tasks, as revealed in the next paragraph.  
It must be noted that a test set was also considered in order to ensure that the developed AI models could perform efficiently not only on the data it was trained on but also on unseen conditions. Generally, the deployment of a test set in ML models composes a critical step in validating the robustness and generalization capability of the model, serving as a simulation of unseen data and providing an unbiased evaluation of the model's  
Figure 5: Architecture of the developed artificial neural network.  
performance. During the training phase, the model learns patterns and relationships from the training dataset, which consists of labeled examples. However, if the model is evaluated solely on this training data, there is a risk of overfitting, where the model memorizes the training data rather than learning to generalize from it. Overfitting leads to poor performance on new, unseen data, even if the model achieves high accuracy on the training set. Hereupon, a test set was also created that demonstrates the ability of the ML models to handle new inputs effectively. Regarding the efficiency of the ANN model, the left part of Figure 6 visually compares the actual target values with the model's predicted outputs on the test set. Theoretically, a perfect model would produce a 45-degree diagonal line, meaning all predictions exactly match the true values, whereas in the case of the developed ANN, only small deviations are observed. Finally, the right part of Figure 6 tracks how the loss function evolves during the training phase, such as the training loss, which displays how well the ANN model fits the training data, while the test loss, which reflects generalization to unseen data.