High-Fidelity MBD Simulation Dataset for Automotive Corner Module Suspension Kinematic Optimization

This dataset contains the high-fidelity multi-body dynamics (MBD) simulation data used for surrogate modeling and multi-objective optimization of an automotive corner-module suspension. The data were generated using ADAMS/Insight based on a three-level full-factorial design of experiments involving eight suspension hardpoint coordinates, resulting in 6561 simulation samples. Each sample contains eight hardpoint coordinates as input variables and three suspension kinematic indicators as output variables. The outputs are the standard deviations of the camber angle, caster angle, and toe angle over the prescribed wheel-travel range. These data were used to train, validate, and test the Neural Network surrogate model described in the associated research article. The dataset is provided to support reproducibility and further research on surrogate modeling, machine learning, and multi-objective optimization for automotive suspension design.