Prediction of Automotive Component Load Configuration Using Best Fit Life Distribution

Abstract

An extreme event such as strong shock resulting from a violation of the hole or a large object on the road can cause damage to vehicle components. As such, the study needs to be done to address the behavior of failure data using the fatigue life and reliability characteristics of extreme fatigue life failure statistical approach. The study also be done by developing a characterization of life distributions based data and configuration best match load to allow a generalized prediction. The research involves testing the fatigue life and cyclic strain fatigue life data generation using Monte Carlo simulation based on the parameters of probabilistic stress cycle curve. Features for all parametric distributions were analyzed by the method of maximum likelihood estimation (MLE) for the generalized extreme value distributions. Assess the suitability of the life distribution for the reliability of extreme fatigue life can be seen through a probability density function. This study found that the developed method capable of predicting the relationship between the load configuration and shape of the distribution of a component failure studied. This approach can contribute to reduced time of experimental testing which is an emphasis in the production process components. This implication provides a particularly significant impact on the development of the automotive industry and enhance the manufacturing sector.