## 3 Results and Discussion
### Creep Assessment
#### 3.3.2. Creep Response--Statistical Analysis
This pattern of Preset Depth (nm) as a dominant feature is also observed in the analysis of Maximum Shear Stress (Gpa) and Strain Rate Exponent, where it stands out as the most significant input variable. A deviation from this pattern occurs in the case of the strain rate sensitivity fFactor, where Loading Speed (mN/s) emerges as the most significant feature, contrasting with the predominant influence of Preset Depth (nm) seen in other output variables. Similarly, Critical Volume for Dislocation Nucleation is another output where Preset Depth (nm) ranks highest in terms of importance. However, when analyzing Net Creep Depth (nm), both Loading Speed (mN/s) and Creep duration (s) are found to be significant, with Loading Speed (mN/s) slightly more influential. These insights from the Random Forest Regression analysis are instrumental in identifying the most predictive input variables for each output variable. Understanding which inputs have the greatest impact on specific outputs can significantly influence decision-making processes in engineering and product design, directing attention to the most influential factors for further investigation or optimization. **Method 3: Partial Dependent Plots from ANNs**  
Figure 16 presents a collection of Partial Dependence Plots (PDPs) generated from an Artificial Neural Network (ANN) model for a variety of output variables. PDPs serve as a graphical tool in machine learning, illustrating the influence of one or two features on the predicted outcome while holding other features constant. These plots effectively demonstrate the relationship between changes in feature values and shifts in the average predicted response. 1. For the output variable Peak Hardness (Gpa), the PDP indicates a consistent rise in predicted hardness with an increase in \"Input Preset Depth (nm)\". This upward trend is also observed for \"Input Loading Speed (mN/s)\" and \"Input Creep duration (s)\", suggesting that increases in these input values are associated with higher predicted hardness values. The analysis of Maximum Shear Stress (Gpa) via PDPs reveals a strong positive correlation with \"Input Preset Depth (nm)\" and \"Input Creep duration (s)\", indicating that higher values of these inputs predict greater shear stress. Conversely, the plot for \"Input Loading Speed (mN/s)\" displays a slight upward trend that eventually plateaus, suggesting a more nuanced relationship with predicted shear stress. Figure 15: Bar charts as the outcome of the Random Forest Regression analysis. Regarding the Output Strain Rate Exponent, the PDP underscores a marked increase in the predicted exponent with a rise in \"Input Preset Depth (nm),\" while the trends for the other inputs are more subdued. 4 The PDPs for the Output Strain Rate Sensitivity Factor exhibit erratic behavior, pointing to potentially complex or non-linear interactions between these inputs and the output, hinting at the intricacies of the underlying relationships. 5 For Output Critical Volume for Dislocation Nucleation, the plots for \"Input Preset Depth (nm)\" and \"Input Loading Speed (mN/s)\" reveal sharp peaks, highlighting specific input values that correspond to drastic changes in the predicted response. In contrast, the plot for \"Input Creep duration (s)\" shows a smoother, more gradual relationship, indicating a different pattern of influence on the predicted outcome. 6 Lastly, the PDPs for Output Net Creep Depth (nm) illustrate increasing trends for all three input variables, with notably steep slopes for \"Input Loading Speed (mN/s)\" and \"Input Creep duration (s)\". This indicates a strong positive relationship between these inputs and the predicted net creep depth, suggesting that higher input values lead to greater predicted creep depths. These PDPs offer insights into the average predicted outcomes based on the dataset under the assumption that the ANN model accurately captures the relationships between input and output variables.