## 7 Results and Discussion
### Dynamic PM on the Friction Welding Use Case  
The analysis of Cases A and B leads us to a crucial conclusion: the optimal \(T_{R}\) depends on the costs associated with each maintenance action. Utilizing a static \(T_{R}\) means the maintenance scheduling policies lack the flexibility to adapt to changes in the working environment or degradation of other components within the system, which could influence the time to failure of the friction welding tools. Moreover, adjusting \(T_{R}\) from a conservative value to one closer to the MTBF may prove efficient if the cost of maintenance actions changes. However, with a conservative approach, estimating the actual MTBF for a given component becomes impossible as components are replaced before failure occurrence.  
To address these challenges, the proposed PMD offers a dynamic solution by continuously adjusting \(T_{R}\). It is initially set at the minimum value observed in the first set of \(B_{a}\). This value dynamically changes each time, based on the time between failures of the last \(B_{a}=4\) friction welding tools replaced, following the rules described in Section 5.2. The time between interventions, \(T_{R}\), increases when no tool within \(B_{a}\) fails and decreases to the minimum time between failures observed if at least one tool in the set of the last \(B_{a}\) fails before being replaced.  
The values of \(B_{a}\) and \(\alpha\) were set based on the costs obtained for the training set when using different combinations of these parameters. As seen in Figure 7, the optimal values for both scenarios A and B were \(B_{a}=3\) and \(\alpha=1.35\); thus, these were the adopted values.  
Figure 8 presents a visual representation of the costs associated with each PM policy for the testing instances under two distinct scenarios, denoted as A and B. The findings demonstrate the superior cost-effectiveness of the proposed PMD approach when compared  
Figure 6: Cost of traditional PM, varying the time between replacements, \(T_{R}\).  
Figure 7: Costs per working cycle on training data according to different values of \(B_{a}\) and \(\alpha\). The yellow point represents the minimum cost per working cycle obtained.  
to conventional PM strategies since, in both Cases A and B, the PMD approach achieves the lowest cost.  
In Case A, as the \(C_{c}\) is much higher than \(C_{p}\), it was expected that the PMC strategy would have good results, but the minimum time between failures observed in the training instances (119 working cycles) is very conservative since the majority of friction welding tools failed after performing more than 500 working cycles. For this reason, the PMMTBF achieves better results than the PMC in both scenarios because the higher exploitation of the useful life of tools compensates economically for the higher number of failures. The dynamic adjustment of maintenance intervals in the PMD strategy emerges as a highly effective mechanism for striking a balance between exploiting assets' lifetimes and minimizing the incidence of failures. As can be seen in Figure 8, this approach outperforms traditional preventive approaches for both cases, achieving cost savings of 19.5% and 51.8%, respectively, for Cases A and B.  
Furthermore, the PMD policy's adaptability shines through as it takes into account the impact of degradation in other system components or shifts in working conditions over time by dynamically adjusting itself based on recent failures and accounting for these evolving circumstances.