Optimal design of a composite leaf spring using genetic algorithms

Abstract

A formulation and solution technique using genetic algorithms (GA) for design optimization of composite leaf springs is presented here. The suspension system in an automobile significantly affects the behaviour of vehicle, i.e. vibrational characteristics including ride comfort, directional stability, etc. Leaf springs are commonly used in the suspension system of automobiles and are subjected to millions of varying stress cycles leading to fatigue failure. If the unsprung weight (the weight, which is not supported by the suspension system) is reduced, then the fatigue stress induced in the leaf spring is also reduced. Leaf spring contributes for about 10–20% of unsprung weight. Hence, even a small amount of weight reduction in the leaf spring will lead to improvements in passenger comfort as well as reduction in vehicle cost. In this context, the replacement of steel by composite material along with an optimum design will be a good contribution in the process of weight reduction of leaf springs. Different methods are in use for design optimization, most of which use mathematical programming techniques. This paper presents an artificial genetics approach for the design optimization of composite leaf spring. On applying the GA, the optimum dimensions of a composite leaf spring have been obtained, which contributes towards achieving the minimum weight with adequate strength and stiffness. A reduction of 75.6% weight is achieved when a seven-leaf steel spring is replaced with a mono-leaf composite spring under identical conditions of design parameters and optimization.