Error tolerance for effective model parameter estimation in multiaxial fatigue life prediction

Multiaxial fatigue life prediction models rely on intrinsic parameters that provide the balance between arbitrary and reference stress/strain conditions. However, this balance may be compromised due to evolving damage mechanisms, causing initially determined model parameters to deviate from actual values, resulting in life prediction errors. Despite the significant impact of fatigue model parameters on prediction accuracy, this issue is often ignored, with many studies assuming constant parameters to simplify prediction algorithms and reduce computational costs. In this study, we introduce a novel approach to quantify the error introduced into fatigue life predictions by approximate methods for determining model parameters under multiaxial loading paths. For the first time, error estimation was conducted using a life-dependent method, revealing that the error is a function of the selected approximation method and the ratio of slope coefficients from S-N curves for torsional versus uniaxial loading. These findings provide a unique framework for selecting computationally efficient approximation methods while balancing life prediction accuracy. This balance is crucial in the design of metallic components using fatigue topology optimization and finite element analysis. The proposed methodology, validated across eight metallic materials subjected to various multiaxial loading paths, offers valuable insights into the trade-offs between computational cost and prediction accuracy, which are essential for optimized structural design.