## 1 Introduction
Recent studies have demonstrated the effectiveness of multi-body dynamic modeling and substructuring techniques for optimizing vehicle vibration and noise control, especially in body mounting systems. [PERSON] et al. (2019) focused on optimizing joint parameters within the LM-FBS framework, using automotive rubber bushings as a case study, showing how changes in parameters, such as those in engine mounts and suspension systems, influence system sound pressure [22]. [PERSON] et al. (2019) employed FBS to reduce road-induced excitation at the body mounting interface, using an inverse substructuring method to estimate the dynamic stiffness modification of bushings for road noise reduction [23]. Since the foundational work of [PERSON] and [PERSON], the field of FBS and receptance coupling has seen significant advancements. Recent developments have expanded these early contributions by integrating novel methodologies and technologies. Notably, [PERSON] et al. (2022) introduced a sophisticated approach that combines receptance coupling substructure analysis (RCSA) with Bayesian machine learning for milling stability predictions [24]. [PERSON] et al. (2013) did consider rotational DoFs in their improved RCSA methodology. This approach incorporates both translational and rotational dynamics in estimating the system's full receptance or FRF matrix. By doing so, it helps predict chatter-free cutting conditions at high frequencies, which is crucial for ensuring stability in high-speed machining processes [25]. Additionally, [PERSON] and [PERSON] (2024) provided a fundamental review of hybrid and modular modeling features and substructuring schemes using RCFBS, highlighting the effectiveness of these techniques for predicting road noise and vibrations in vehicle dynamics [26]. In another study, [PERSON] and [PERSON] (2024) demonstrated that the receptance coupling Frequency-Based Substructuring (RC-FBS) method offers superior accuracy for high-frequency dynamics, particularly in systems with flexible or distributed parameters. Their findings reveal that the RC-FBS method achieves stronger alignment with finite element analysis (FEA) and direct solution techniques compared to traditional modal analysis, largely due to mitigating mode truncation issues common in modal approaches. Additionally, RC-FBS provides more precise results during the decomposition process, particularly when subsystems exhibit significant flexibility, and excels in predicting both rotational and translational dynamics in complex systems. These capabilities make RC-FBS a significant advancement for modeling dynamic systems across high-frequency domains [27]. The authors extend the application of this method to the development of a half-vehicle model, enabling modular coupling of dynamic interactions between subsystems like the chassis and suspension. By consolidating substructures like the front and rear suspensions into a single module with a sparse matrix representation (minimizing off-diagonal term inversions), this method reduces unnecessary computational complexity and enhances model reliability. This model reduction strategy can also minimize potential noise from off-diagonal elements appearing in experimental data, preserving boundary conditions and improving stability for practical, computationally efficient applications [28]. The novelty of this paper lies in advancing the field of FBS and generalized receptance coupling Frequency-Based Substructuring (GRCFBS) by addressing key challenges and expanding their applicability to NVH modeling. One major contribution is the scalability of the GRCFBS method, which extends a unified coupling framework to accommodate diverse subsystem configurations while maintaining the same number of substructures and interface mappings. This scalability enables flexible applications critical for NVH tasks, such as target setting, cascading, system identification, and subsystem decomposition.