A high cycle fatigue numerical framework for component-level virtual fatigue testing: Application to a light-duty vehicle lower control arm

The conception of lightweight structures constitutes a fundamental design principle of the contemporary automotive industry. In this sense, the chassis is one of the areas with major potential for the implementation of solutions for mass reduction, as it accounts for around 25% of the total weight of a regular road vehicle. When it concerns the design process, the fatigue strength of the components that integrate this system represents a factor of utmost importance because these parts are usually subjected to a high number of load cycles throughout the vehicle service life. In this work, a numerical framework to perform fatigue high-fidelity simulations of metallic engineering structures such as chassis components is presented. This framework builds upon an isotropic damage-based high cycle fatigue constitutive model coupled to an advance in time strategy algorithm used to improve the computational efficiency of the simulations. A novel methodology to calibrate the model material parameters consistent with the local nature of the fatigue phenomenon is proposed. Two different durability tests carried out with the steel lower control arm of a light-duty vehicle are virtually reproduced to assess the ability of this numerical framework to replicate the main features of real experiments. The performed simulations show the capability of the present numerical scheme to accurately capture the location of the fatigue cracks while providing a physically sound representation of their morphology. In addition, they also provide an excellent estimation for the number of cycles quantified in both experiments for the propagation of the cracks. The obtained results evidence the predictive capabilities of this numerical framework in performing fatigue high-fidelity simulations of metallic structures at engineering-relevant scales, which potentially allows the reduction of the number of experiments required to support engineering decision-making processes.