Fatigue Life Prediction Based on Probabilistic Fracture Mechanics: Case Study of Automotive Parts

Abstract

This paper studies the stochastic behavior of fatigue crack growth analytically and empirically by employing basic models in fracture mechanics. The research estimates the crack growth rate probabilistically, quantifies the uncertainty of probabilistic models under fatigue loading in automotive parts, and applies the simulations on W319 aluminum alloy, which has vast applications in automotive components’ products. Walker and Forman correlations are used in the paper. The deterministic simulations of these models are verified with afgrow code and validated experimentally with fatigue data of W319 aluminum. Then, the models are treated probabilistically by considering the models’ parameters stochastic. Monte Carlo (MC) simulation is employed to investigate the models under stochastic conditions. The paper is quantifies the propagation of uncertainty with calculating the standard deviations of crack lengths via cycles. The proposed procedure is useful for selecting a proper probabilistic fatigue crack growth model in specific applications and can be used in future fatigue studies not only in the automotive industry but also in other critical fields, to obtain more reliable conclusions.