## 6 Limitations  
The Frequency-Based Substructuring (FBS) method, multi-body dynamic (MBD) modeling, and finite element analysis (FEA) offer distinct advantages depending on the system and objectives of analysis. The FBS approach reduces the complexity of dynamic systems by operating in the frequency domain, making it efficient for systems with small deformations and linear behavior. It simplifies the analysis by coupling subsystems' FRFs, enabling hybrid modeling with computational efficiency. However, FBS is not suitable for systems involving large motions or significant non-linearities due to its reliance on linear superposition principles. MBD methods, on the other hand, are designed for analyzing non-linear dynamics, large motions, and contact mechanics in the time domain. While MBD is a versatile technique, its simulations are computationally intensive and highly sensitive to time-step sizes. FEA provides detailed spatial resolutions of system behavior and is widely used for static and dynamic structural analysis. Unlike FBS, which focuses on system decomposition, and MBD, which emphasizes motion dynamics, FEA excels in capturing localized stress and strain but can become computationally expensive for large-scale or highly dynamic systems due to its reliance on extensive datasets. Together, these methods complement each other, with FBS offering efficiency for reduced-order modeling and predicting system responses--particularly in the early stages of development when the entire system is not available, but subsystems' receptances or FRFs can be measured and utilized effectively.  
While the proposed methodology is highly effective for linear and lightly non-linear systems, it faces challenges in handling highly non-linear systems or complex damping scenarios. The framework excels in systems exhibiting mild non-linearities, where response predictions can be relatively standardized. However, as non-linear interactions such as hysteresis, complex phase damping, or highly non-linear joints become significant, the GRCFBS approach's applicability is not valid because the diverse nature of non-linear interactions and interfaces makes standardization difficult. The accuracy of the results is also highly dependent on the quality of measured FRFs in the experimental approach, with noise and non-linearities at interface connections posing additional challenges. Future developments could address these limitations by incorporating advanced non-linear substructuring techniques or leveraging machine learning to predict and correct non-linear behaviors. Moreover, the framework is most efficient for modular, reconfigurable systems, where substructuring assumptions are true. As non-linear behaviors grow more complex across subsystems, more sophisticated methods will be required to maintain prediction accuracy.  
In systems with significant non-linearities, the methodology using substructuring technique relies on approximations, such as linearizing non-linear joints or representing local non-linear dynamics as additional dynamic stiffness. These can be incorporated as \"black-box\" subsystems using experimental receptance measurements at critical points. Current research efforts aim to develop algorithms capable of integrating complex non-linear dynamics into substructuring techniques, but such advancements are still under development.