The knowledge of a vehicle's center of gravity (COG) and moment of inertia (MOI) are important from vehicle dynamic and comfort points of view. These parameters can be determined from the CAD model of the vehicle, however, manufacturing inaccuracies and additional elements can modify the values, and experimental validation can be necessary. The determination of COG (and even MOI) can be carried out using classical physical methods, but a more convenient and sophisticated method is offered based on dynamic testing data. The method is known and accessible even in commercial testing software, but a clear recommendation for the optimal input data and the expected accuracy is still not available. The purpose of this study is to define the influencing factors of the measurement and quantify their effect. The introduced method is based on the evaluation of the frequency response functions in the mass-line region. Input data were obtained by using impulse hammer excitation and accelerometers for measuring the response of the structure. The measurements and the evaluations were performed in Siemens LMS Test.Lab software using Rigid Body Calculator module. The results of the dynamic measurement are compared to CAD data and to the COG value got from simple static measurement.

This paper presents the design process of a composite wheel rim for a Formula Student race car. First, the design requirements for composite rims are outlined, which are driven to ensure safe operation as well as compliance with race regulations. Next, a novel methodology for the iterative design of composite wheel rims is proposed, and its steps are individually presented. The load cases were determined using data from lap time simulations and from practical experience from the operation of previous race cars. Material cards for the simulations were created by measuring the characteristics of the prepreg composites. The geometry of the rims was created by prioritizing the optimum contact with the tires. After creating the rim geometry, the composite material cards, and the simulation’s pre-processing, the layup iteration process began. In this manual iterative process, FEM simulations were run and their results were evaluated. The desired component properties were reached after 11 layup iterations. The final result is a weight reduction of 35% compared to the aluminum rims and 15% compared to the previous multi-piece CFRP rims, without a compromise in operational safety. The main novelty of the paper is the description of the iterative layup selection logic and process in detail, as well as demonstrating this on a concrete use case.

Although numerous studies have used ML to predict gear transmission error, few have provided a normalized, interpretable risk metric for early tolerance assessment. This work fills that gap by proposing the Tonal Risk Index (TRI). Kinematic Transmission Error (KTE) is a well-established primary excitation source of tonal gear noise in electric vehicle drivetrains. This study introduces the TRI, a novel, dimensionless indicator that quantifies relative tonal noise risk directly from predicted KTE values. We employ a large-scale dataset of 39,984 Monte Carlo simulations comprising 15 manufacturing tolerance and process-shift variables, with KTE values as the target. Baseline linear regression failed to capture the strongly non-linear relationships between tolerances and KTE (R² ≈ 0), whereas non-linear models—Random Forest and XGBoost—achieved high predictive accuracy (R² ≈ 0.82). Feature importance analysis revealed that pitch error, radial run-out, and misalignment are consistently the most influential parameters, with notable interaction effects such as pitch error × run-out and misalignment × form-defect shift. The TRI normalises predicted KTE values to a 0–1 scale, enabling rapid comparison of tolerance configurations in terms of tonal excitation risk. This approach supports early-stage design decision-making, reduces reliance on high-fidelity simulations and physical prototypes, and aligns with sustainability objectives by lowering material usage and energy consumption. The results demonstrate that data-driven surrogate models, combined with the TRI metric, can effectively bridge the gap between manufacturing tolerances and NVH performance assessment.