Abstract
This paper presents the optimization of spring fatigue life associated with suspension system parameters using the design of experiment approach. The effects of suspension parameters on spring fatigue life were analyzed because this process can improve spring fatigue life from a distinct perspective. A quarter car model simulation was performed to obtain the force time histories for fatigue life prediction where the suspension parameters were adjusted. Multiple input regression and interaction plots were conducted to identify the interaction between these parameters. A full factorial experiment was performed to determine the optimal suspension settings that would maximize the spring fatigue life. For the regression, a high R 2 value of 0.9078 was obtained, indicating good fitting. The established regression showed normality and homoscedasticity for consistent prediction outcome. Reducing the spring stiffness and sprung mass while enhancing the damping coefficient is therefore suggested to enhance fatigue life.
Highlights
Automobile springs are subjected to repeated cyclic loading during their operation
The spring fails when a crack is initiated under cyclic loading, and this phenomenon is known as fatigue failure
design of experiment (DoE) is the statistical approach used in this work to identify the effects of suspension parameters on the spring fatigue life and the settings of these parameters to optimize the response
Summary
Automobile springs are subjected to repeated cyclic loading during their operation. The spring fails when a crack is initiated under cyclic loading, and this phenomenon is known as fatigue failure. Fatigue analysis is a main concern during automotive suspension design. Durability analysis of components is usually conducted through simulations before experimental verification [1]. During the fatigue simulation and analyses, the geometry, material properties, and loadings are the main key inputs to the fatigue life predictions [2]. A critical part of automotive component fatigue life assessment is determining the load associated with the road irregularities [3]. Unrealistic loadings lead to inaccurate automotive design, given that the latter is based on the former
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