LAUSR

Abstract This study aims to determine the fatigue reliability of an automotive crankshaft by stochastically inducing random loads because of limited experimental data. Given the difficulties in capturing actual load–time history data from laboratory or field-testing and the lack of loading history data, the corresponding stochastic modellings are formulated. The maximum and minimum loads obtained from the automobile industry at various rotational speeds are stochastically induced to generate random loads for assessing fatigue reliability. Fatigue life is evaluated using the Coffin–Manson, Morrow and Smith–Watson–Topper models in the time domain using the rainflow cycle counting technique. Weibull distribution is identified as the best fit of the data for fatigue lives on the basis of Akaike’s information criterion and is used to model reliability and hazard rate. Reliability below 0.09 with a hazard rate of over 1.35 × 10−5 is proposed as the high-risk regions according to the mean number of cycles to failure obtained from the fatigue life models. Thus, stochastic-induced random loads can assess the hazard rate-reliability from fatigue life data to predict the durability and structural integrity of components.