## 4 Discussion  
The RCFBS approach shows promise in predicting dynamic responses for systems with continuous and lumped-mass properties. However, it has several limitations:  
* Assumption of Linearity: RCFBS relies on frequency-based substructuring, which operates in the frequency domain. This approach assumes linear dynamic behavior, which may lead to inaccuracies in systems with non-linear characteristics, such as large deformations or complex materials.
* Sensitivity to Measurement Errors: The accuracy of RCFBS heavily depends on the quality of the receptance/FRF data from experiments or simulations. Errors in measuring translational or rotational receptances can propagate through the model, resulting in incorrect dynamic response predictions. Effective filtering is crucial for reliable experimental FRF measurements.
* Non-Modular Systems: The RCFBS method may be less effective for inherently non-modular systems. Complex geometries or dynamically changing boundary conditions can complicate the application of receptance coupling and substructuring techniques.
* Complex Interactions: In systems with intricate component interactions, simplifying the system into subsystems may overlook important dynamic complexities. Highly coupled components or significant non-linearities may require a more holistic modeling approach than RCFBS can provide.  
Understanding these limitations is vital for interpreting the results and identifying areas for future research. Potential improvements could involve developing methodologies that address non-linear behaviors, enhancing measurement techniques, or integrating holistic modeling approaches.  
Despite these challenges, the RCFBS method demonstrates reliable performance in the case studies, particularly within the frequency ranges relevant for industrial applications. Traditional methods like the modal approach face limitations, such as mode truncation at higher frequencies with distributed parameters, whereas RCFBS effectively captures these dynamics. Comparisons with FEA indicate that RCFBS can achieve similar accuracy with a significant reduction in computational load, making it a viable option for dynamic systems needing efficient and flexible solutions.  
In the first case study, four primary modes are considered to capture the essential system dynamics in the modal analysis. While these modes provide useful approximations, they may not encompass all complexities, especially at higher frequencies where higher-order modes become influential. This limitation emphasizes the advantage of RCFBS, which integrates all relevant modes without truncation, thus enhancing predictive accuracy for systems with distributed parameters. Furthermore, the RCFBS approach leverages the frequency response function (FRF), which captures all mass, damping, and stiffness properties of the system. This comprehensive representation enables more accurate dynamic response predictions, particularly in systems with distributed parameters. In contrast, the modal method often relies on a limited number of modes, potentially missing complex interactions, especially in the high-frequency range. By leveraging the FRF, RCFBS integrates the full dynamic characteristics of the system, leading to enhanced prediction reliability.