Improved Design Optimization of a Composite Leaf Spring using Swarm Intelligence

摘要

To cope with the needs of natural resources conservation, automobile manufacturers are attempting to reduce the weight of vehicles in recent years. The suspension system in an automobile significantly affects the behavior of the vehicle related to riding comfort, stability ... etc. Leaf spring is one of the potential elements used in Suspension system for the weight reduction in automobiles as it leads to the reduction of unsprung weight of automobile. An advanced natured inspired technique Ant Colony Optimization (ACO) [1,2] (a subset of Swarm Intelligence) for design optimization [3]of composite leaf springs is proposed here. 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[4]. 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 ACO approach for the design optimization of composite leaf spring. On applying the ACO, the optimum dimensions of a composite leaf spring have been obtained, which contributes towards achieving the minimum weight with adequate strength and stiffness. A considerable reduction of weight is achieved when a seven-leaf spring is replaced with a mono-leaf composite spring under identical conditions of design parameters and optimization.