## 5 Future Directions in Research  
### Integration of Robustness in System-Level Design Optimization  
The effects of laser cutting or punching during manufacturing and assembly lead to material degradation in the machine, particularly in areas with sharp edges such as stator and rotor poles. This shape distortion impacts key parameters, like torque ripple, efficiency, and losses [80,81]. In general, manufacturing imperfections should be accounted for during the design phase, especially in drive system design for specific applications like EVs, wind power generation and aeronautical applications, as they also influence controller performance. The robust-oriented approach follows this line of thinking and incorporates these considerations into the design process by minimizing the sensitivity to parameter variations, thereby ensuring that such deviations do not affect the smooth operation of the machine. This is also illustrated schematically in Figure 14, where a minor manufacturing deviation in the deterministic solution results in a significant impact on the outcome, as indicated by \(\Delta_{\mathrm{f1}}\). In contrast, the robust solution exhibits smaller deviations, which can ensure the reliable operation of the system.  
Reference [65] is a study that follows up on previous research by integrating the concept of robustness into the SLDOM. The authors introduce the Design for Six Sigma (DFSS) approach to avoid solutions derived from a purely deterministic algorithm, which could lead to high-risk designs for engineers due to higher number of defections and Probability of Failure (POF), influenced by the sensitivity to variations. While the results are slightly inferior to those obtained using a deterministic approach, as can be seen in Figure 14, they exhibit greater robustness and stability. Although this study may not specifically address SRM drive systems, it highlights the significance of system-level optimization in conjunction with the SSOM and its potential implementation in SRM drives.  
References [61,62] utilized the Taguchi method for this purpose. In [82] a modification of the Taguchi method is presented for integration into System-Level optimization. This novel System-Level Sequential Taguchi Method (SLSTM) aims to achieve robust optimization of the entire drive by selecting a set of design parameters for both the controller and the machine, using sensitivity analysis and evaluating them through the Signal-to-Noise (S/N) ratio to ascertain robustness. The Taguchi method optimizes this set of parameters, and after just three iterations, the target is achieved. Finally, an analysis of the Component-Level Sequential Taguchi Method (CLSTM) is performed, comparing it with SLSTM. The results indicate that SLSTM outperforms CLSTM in terms of torque ripple, torque, and losses. Similar results are presented in [83]. The authors of this paper anticipate that this method will gain widespread adoption in the future, as SRM drive research continues to  
Figure 14: Deterministic and robust solutions in the optimization problem.  
advance and finds industrial applications--particularly in EVs and small electric bikes, where robustness and reliability from a manufacturing standpoint are critical.