## 3 Results and Discussion
### Statistical Reliability--Weibull Analysis  
The Weibull analysis is a statistical method that quantifies the reliability of medical devices, including implants and biomedical alloys. It identifies the causes of failures, predicts lifespan, and contributes to the development of safe, long-lasting implants for patients. The aforementioned process facilitates the enhancement of designs by identifying critical failure points and comparing the reliability of different implant materials. This is achieved by determining failure and failure rates. In this study, a reliability analysis was conducted to ascertain the optimal B4C reinforcement ratio that should be incorporated into the implant material. This analysis was undertaken to facilitate a comparison of the degradation rate behavior and durability of different materials. The reliability results yielded a prediction of the clinical performance of the implant materials with varying additive ratios under real load conditions in the patient's body. Thus, it was to reduce the clinical failure and revision risk levels. Table 1 provides the calculated Weibull shape (\(\beta\)) and scale (\(\eta\)) parameters for each alloy group and output property. The shape parameter is indicative of distribution consistency, while the scale parameter is associated with the characteristic performance level for each property. These values support the graphical results presented in Figures 21-23 and allow for a quantitative comparison of the reliability and performance of the studied alloys. CoCrMo demonstrates the highest mean hardness (\(\eta\) = 364.71) with moderate variability (\(\beta\) = 12.80). The 17-4 PH alloy exhibits the highest degree of consistency (\(\beta\) = 26.09), suggesting reliable hardness properties that are well-suited for predictable biomedical applications (Figure 21a). The Ti-based alloys exhibited the highest mean wear (\(\eta\) = 0.74), indicating a significantly higher propensity for degradation under mechanical stress. In contrast, CoCrMo alloys exhibit the lowest mean wear (\(\eta\) = 0.28), suggesting their superior resistance to wear in biomedical applications that demand durability (Figure 21b). CoCrMo allows demonstrate the highest degree of stiffness (\(\eta\) = 207.91), a property that is optimal for applications requiring mechanical robustness. The employment of Ti alloys in the domain of orthopedic implants is advantageous due to their considerably lower modulus (\(\eta\) = 105.62), which results in a reduction in stress shielding (Figure 22a). \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Alloy Group** & **Shape (\(\beta\))** & **Scale (\(\eta\))** \\ \hline \multirow{3}{*}{Hardness (HV3)} & CoCrMo & 12.7953597 & 364.7145955 \\  & Ti & 12.6234387 & 325.8027728 \\  & 17-4 PH & 26.0860912 & 324.8207463 \\ \hline \multirow{3}{*}{Wear (\%)} & CoCrMo & 2.23474206 & 0.275447669 \\  & Ti & 2.41007883 & 0.741899597 \\  & 17-4 PH & 1.57004846 & 0.430016353 \\ \hline \multirow{3}{*}{Elastic Modulus (GPa)} & CoCrMo & 64.7096197 & 207.9113035 \\  & Ti & 26.1857497 & 105.6228492 \\  & 17-4 PH & 41.9605587 & 184.473096 \\ \hline \multirow{3}{*}{Electrical Conductivity (\%IACS)} & CoCrMo & 16.0086449 & 41.13308984 \\  & Ti & 8.89978422 & 1.086464231 \\  & 17-4 PH & 51.5724945 & 1.98040602 \\ \hline \multirow{3}{*}{Corrosion Rate (mm/year)} & CoCrMo & 2.29527297 & 1.508012127 \\  & Ti & 2.5916634 & 1.212344711 \\  & 17-4 PH & 3.58370088 & 1.957594188 \\ \hline \multirow{3}{*}{Surface Oxide Resistance (\(\Omega\) cm\({}^{2}\))} & CoCrMo & 12.056819 & 368.4549732 \\  & Ti & 22.6267154 & 388.8891863 \\ \cline{1-1}  & 17-4 PH & 21.9032426 & 302.1125258 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Weibull parameters for mechanical and corrosion properties. Figure 23: Weibull probability plot for (**a**) corrosion rate (mm/year); (**b**) surface oxide resistance (\(\Omega\) cm\({}^{2}\)). Figure 22: Weibull probability plot for (**a**) elastic modulus (GPa); (**b**) electrical conductivity (%IACS).