Accurate representation of strain-rate dependent stress–strain behavior is essential for reliable structural analysis and crashworthiness assessment in automotive engineering as vehicle structures and safety-critical components experience a wide range of loading conditions, from quasi-static deformation during forming and durability events to high strain-rate loading during crash and impact scenarios. Conventional material characterization approaches rely on extensive experimental testing to generate strain-rate dependent constitutive data, including specialized high strain-rate techniques that are costly, time intensive, and require dedicated infrastructure, which often limits the availability of comprehensive material data during early vehicle development phases and supplier evaluation. This paper presents a data-driven methodology for predicting strain-rate dependent stress–strain behavior of automotive steels using limited experimental input data. The proposed approach integrates physically meaningful material properties with a machine-learning framework to construct a generalized stress prediction model that remains consistent with classical material mechanics principles. Fundamental parameters governing material behavior, including strain, strain rate, yield stress, density, Poisson’s ratio, and elongation limit, are used as inputs to capture the combined influence of deformation state, loading rate, and intrinsic material characteristics. A Random Forest regression algorithm is employed to model nonlinear relationships without relying on predefined analytical hardening or rate-sensitivity formulations. The methodology is developed and validated using experimentally obtained stress–strain data from multiple automotive steel grades under quasi-static and dynamic loading conditions, and model performance is evaluated through correlation analysis between predicted and experimental stress responses across the full strain range. The results demonstrate strong agreement with experimental observations and robust generalization capability across different materials and strain-rate regimes, with minor deviations observed at very high strain levels attributed to limited data density near fracture conditions.
The proposed framework significantly reduces dependence on extensive physical testing while maintaining accurate representation of material behavior, providing a practical and efficient alternative for material characterization that supports faster material selection, reduced testing effort, and improved robustness of automotive computer-aided engineering simulations.