## 4 Results
 \\  & Maximal Depth & [2; 3; 4; 5; 6; 7; 8; 9; 100; 101; 102; 103; 104; 105; 106; 107; 108; 109; 110; 111; 111; 112; 113; 114; 115; 116] & 10 \\ \hline \multirow{3}{*}{LR} & Regularization & Ridge, Lasso & Ridge \\  & Lambda & [\(10^{-6}\)–\(10^{2}\)] & 0.0034389 \\  & Multi-class coding & One-vs.-All, One-vs.-One & One-vs.-One \\ \hline \multirow{3}{*}{SVM} & Kernel scale & [0.001–1000] & 1.1685 \\  & Box constraint level & [0.001–1000] & 551.7341 \\  & Multi-class coding & One-vs.-All, One-vs.-One & One-vs.-One \\ \hline \multirow{3}{*}{K-NN} & Number of neighbors & [1; 2; 3; 4; 5; 6; 7; 8; 9; 100; 101; 102; 103; 104; 105; 106; 107; 1087] & 12 \\  & Distance metric & Chebyshev, Euclidean, Minkowski & Euclidean \\ \hline \multirow{3}{*}{RF} & Number of learners & [10; 11; 12; 13; 14; 15; 16; 17; 18; 19; 200; 201; 202; 203; 204; 205; 206; 207; 208; 209; 210; 211; 223; 2011; 212; 213; 2012; 214; 215; 216; 2173] & 170 \\ \hline \multirow{3}{*}{ANN} & Number of fully connected layers & [1; 2; 3] & 2 \\  & Activation function & ReLU, tanh,  
As illustrated in Table 5, the ANN model demonstrates superior performance across all metrics when identifying the correct material feeding method for the validation dataset. The RF model demonstrates strong performance with consistent metrics, while the K-NN model performs well but falls short of the RF and ANN models. The LR model is a simple, interpretable model, but it might not capture complex patterns in the validation data compared to the RF or ANN models. The DTs are prone to overfitting and might not generalize well without pruning or ensemble techniques, and the SVM model has the lowest performance metrics compared to the other ML models. In the validation dataset context, the ANN achieves the highest accuracy (95.6%) and superior performance in all performance metrics. The RF model exhibits a noteworthy accuracy of 94.3%. LR and K-NN demonstrate commendable performance, achieving 85.1% and 87.2% accuracy, respectively. In contrast, the performance of the SVM and DT is comparatively lower, with the SVM demonstrating the least optimal results (81.9% accuracy). Notably, the performance of the ANN notably improves to 97.82% accuracy after parameter optimization. K-NN enhances performance (87.2% to 89.36% accuracy), signifying its sensitivity to parameter optimization. On the other hand, the SVM exhibited modest gains in precision but encountered difficulties in maintaining recall, underscoring challenges in classifying certain classes even after parameter optimization. The performance of RF, which reached 94.3% accuracy without parameter optimization, slightly declined to 93.61% accuracy with parameter optimization. Despite the RF model demonstrating optimal performance for the training set, the ANN model exhibits superior performance for the validation set compared to other ML models. The performance of the RF model on the validation set is observed to decrease, and this decline, occurring on the validation data despite robust outcomes on the training set, may suggest the occurrence of overfitting for the RF model. In order to identify the impact of input parameters on the decision-making process concerning the selection of material feeding methods, a Shapley value analysis of the input parameters was conducted. Table 6 presents the Shapley values of the input parameters with respect to the material feeding method. Figure 4 illustrates the graphical summary of the Shapley values for the ANN model.