## 4 Result
### Result of ML Techniques
#### 4.2.4 Artificial Neural Networks (ANN Model)  
The ANN model was created in the R environment using the NeuralNetTools and caret packages. The network architecture consisted of a single input layer with 4 neurons that correspond to the input variables (H, W, I, S). This was followed by one hidden layer with 5 neurons, ensuring complete transformations of the input data. The output layer contained a single neuron that represented the predicted continuous output variable F (Figure 13). A linear activation function (linout = TRUE) was used for the output layer, suitable for regression tasks where the goal is to predict a continuous value. The key hyperparameters used during the training included the range of initial random weights (rang = 0.1), the maximum number of iterations (maxit = 500), and the regularization parameter (decay = 0.01), which helps prevent the model from becoming overtrained. The training was performed using 5-fold cross-validation, which enabled a more reliable evaluation of the generalization ability of the model. The results showed that this architecture provides a good ability to approximate the relationships between inputs and outputs on the given dataset. Each line between the neurons represents a connection, with the values assigned to them indicating the weights of the connection. Where the line and the weights are black it indicates positive weights, i.e., if the value of the neuron at the beginning of the line increases, the value of the neuron at the end of the line also increases (with other inputs constant). The blue lines represent negative weights (if the value of the neuron at the beginning of the line increases, the value of the neuron at the end of the line decreases with other inputs constant). The number of iterations, or steps, that the training model performed is 24,463. The Error value represents the Sum of Squared Errors (SSE) that the network reached on the training data at the end of the optimization process. A lower value means better adaptation of the model to the training data. The importance of individual input variables in the creation of the model is determined using weights of importance (Figure 14) The most important variable is the height of the impact of the hammer (0.367), which contributes 36.7% to the overall decision. With a very small difference in importance, the strength of the conveyor belt follows (0.346). A variable with a lower weight is weight of the falling hammer, with (0.229). The least relevant variable is considered to be the impactor of the hammer (0.057). \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**No.** & **H (m)** & **W (kg)** & **S (N\(\cdot\)mm\({}^{-1}\))** & **I** & **F (kN)** & **Fp (kN)** & **Error** \\ \hline
1. & 1.6 & 100 & 2000 & 0 & 30.718 & 26.656 & 4.452 \\
2. & 1.6 & 100 & 1000 & 0 & 30.026 & 25.981 & 4.045 \\
3. & 2.0 & 80 & 1000 & 0 & 34.451 & 30.543 & 3.908 \\
4. & 2.0 & 90 & 2000 & 0 & 31.632 & 28.286 & 3.346 \\
5. & 2.6 & 60 & 2000 & 1 & 26.103 & 23.647 & 2.456 \\
6. & 2.6 & 60 & 2000 & 0 & 25.775 & 23.325 & 2.450 \\
7. & 2.4 & 80 & 630 & 1 & 28.422 & 25.986 & 2.436 \\
8. & 2.2 & 90 & 630 & 0 & 28.647 & 26.289 & 2.357 \\
9. & 2.4 & 80 & 1000 & 0 & 32.458 & 30.217 & 2.241 \\
10. & 1.2 & 50 & 630 & 0 & 9.229 & 11.463 & 2.234 \\ \hline \hline \end{tabular} Note: Fp—predicted values of impact force, F—empirical values of impact force.  
\end{table} 
Table 5: Top 10 Prediction Errors Compared to Real Values. Figure 15 shows a graph of the actual values and predicted values that were obtained from the test group. The line represents the ideal prediction. Figure 14: Importance of variables—ANN model (output from the program R). Figure 13: ANN model. The accuracy of the model for prediction was verified using the test group. The results MSE = 0.248 and MAE = 0.383 show that the model created is very good and can explain a significant part of the variability.