## 4 Results and Discussion
### Machine Learning Models
Therefore, for applications that require consistent and highly accurate performance across all categories, the SVM would likely be the optimal choice. However, the Bagged Trees and Neural Network models remain strong contenders, depending on the specific requirements of the task. Partial dependence analysis of input parameters and predicted defect categories (D1-D6) (Figure 7) in the optimal neural network (NN) model allows for the identification of pivotal production parameters that exert the most substantial influence on the incidence of diverse defect types. The following section provides a comprehensive account of the influence of each parameter on production quality. Furthermore, the production temperature (X1) (Figure 7a) has been identified as a significant factor that influences the occurrence of cracks (D2). As illustrated in the graph, an increase in temperature above 1020 \({}^{\circ}\)C is associated with a notable increase in the predicted value for defect D2, indicating that elevated temperatures can contribute to an enhanced likelihood of cracking. In the case of defects such as surface irregularities (D3) and radial runout (D4), the predicted value exhibits a slight decrease at higher temperatures, which may be indicative of a reduced likelihood of occurrence at elevated temperatures. On the contrary, defects such as scratches (D1) and improper hardness (D6) are practically unaffected by temperature changes. This suggests that controlling the production temperature is vital to reducing the incidence of cracks. The type of coolant used (X2) (Figure 7b) has a significant impact on the incidence of scratches (D1). A notable reduction in the predicted value for defect D1 was observed when the coolant was changed from type \"A\" to type \"O\", from 0.7 to nearly 0. This suggests that utilising an appropriate coolant can markedly diminish the likelihood of scratches. The remaining defects (D2, D3, D4, D5, D6) exhibit low predicted values and minimal variation dependent on the type of coolant, suggesting that this parameter exerts a relatively limited influence on these defects. The analysis of the relationship between the parameter X4 (Figure 7c) and defects indicates that it has a significant impact on the occurrence of cracks (D2) and dimensional problems (D5). Regarding cracks, the predicted value for defect D2 shows a marked increase when X4 exceeds 0.35, suggesting that the control of this parameter may be a crucial factor in the reduction of cracks. Similarly, defect D5 (dimensional inaccuracies) exhibits a maximum at medium X4 values, indicating that maintaining X4 within a specific range can help ensure dimensional accuracy. The influence of X4 on other defects, such as D1 (scratches), D3 (surface irregularities), and D6 (hardness problems), is less pronounced. The categorical parameter (X5) exerts a moderate influence on a range of defects. The predicted values for defects D2 (cracks) and D5 (dimensional inaccuracies) demonstrate a moderate increase depending on the category of X5, indicating a potential relationship, albeit less pronounced than for other parameters. The remaining defects demonstrate a relatively stable response to changes in X5. The wear of the tool (X7) (Figure 7d) has a significant impact on the incidence of cracks (D2). Upon reaching a high level of tool wear (\"H\"), the predicted value for defect D2 exhibits a notable increase, reaching 0.8. This indicates that tools that show suboptimal conditions represent a significant contributing factor to the appearance of cracks. On the contrary, the predicted values for other defects, including surface irregularities (D3), radial runoff (D4), and improper hardness (D6), exhibit greater stability and less sensitivity to this parameter. This suggests that prioritising the control of tool condition may be an effective strategy to reduce the occurrence of cracks, while other defects appear to be less dependent on this parameter. Figure 7: Partial dependence analysis for the NN model. (a) X1 vs. Defect, (b) X2 vs. Defect, (c) X4 vs.