## 3 Results
### Statistical Analysis
This increase indicates a rougher surface with a more pronounced texture as a result of applying a higher force. The average roughness, Ra, demonstrates the least variation among the three indicators, suggesting a reduced sensitivity to load changes. Ra values increase gradually from 1.5 \(\upmu\)m at 0 N to 2 \(\upmu\)m at 2 N, reaching 3.5 \(\upmu\)m at 5 N. However, we note an increase in the standard deviation for Ra with load, suggesting greater variability in the average roughness at higher loads. The total profile height, measured by the Rt parameter, shows a moderate increase with load. Rt values evolve from 7 \(\upmu\)m at 0 N to 8.5 \(\upmu\)m at 2 N and reach 12 \(\upmu\)m at 5 N. Unlike Ra, the standard deviation for Rt remains relatively constant, indicating a similar variability of the total profile height regardless of the applied load. The average roughness height, represented by the Rz parameter, proves to be the indicator most sensitive to load variations, demonstrating the most significant increase. Rz values increase from 9 \(\upmu\)m at 0 N to 11 \(\upmu\)m at 2 N, reaching 16 \(\upmu\)m at 5 N. Similar to Ra, the standard deviation for Rz increases with load, suggesting greater variability of the average roughness height at higher loads. These observations suggest that applying a higher force during processing or surface usage leads to a rougher texture, and the different sensitivities of Ra, Rt, and Rz parameters reflect how they are defined and measured. The increase in standard deviation for Ra and Rz indicates reduced roughness uniformity at higher loads, while the numerical values in the table provided a solid basis for interpreting the graphs. These findings have significant practical implications for optimizing processing, evaluating surface wear, and selecting suitable materials for various applications, depending on specific roughness requirements. To enhance the reliability of the results, 90% confidence intervals were calculated for the reliability values using the bootstrap method. These intervals provide an estimation of the uncertainty associated with the reliability values. The bootstrap method is a resampling technique that allows the estimation of the distribution of a statistic, in this case, reliability, by repeatedly resampling the observed data. In the analysis performed to determine the confidence intervals, a number of 1000 bootstrap samples were generated, each having the same size as the original sample, but through resampling with replacement from the original data. For each bootstrap sample, the reliability value was calculated at the time points of interest. The 5 th and 95 th percentiles of the distribution of reliability values calculated for the bootstrap samples were computed. These percentiles define the 90% confidence interval. The results generated by the Python 2.0 code provided an estimation of the mean reliability and the 90% confidence intervals for each component (knife, tooth,  
Figure 16: Variation in roughness with standard deviation zone: (a) Ra variation with standard deviation zone; (b) Rt variation with standard deviation zone; (c) Rz variation with standard deviation zone. through) at specific time points (Table 11). These intervals help us understand the uncertainty associated with the reliability estimations. The results of this analysis clearly demonstrate a downward trend in reliability for all three components (knife, tooth, trough) as operating time advances. This highlights the pronounced wear and tear to which these components are subjected under the working conditions of the Patars quarry. The reliability of the knife decreases from 0.70 at 30 days to 0.20 at 90 days, indicating rapid wear. The excavator tooth maintains a higher reliability throughout the analyzed period compared to the knife and the sorting station trough. This is evident through the higher average reliability values of the excavator tooth across the entire analyzed period.