The Optimal Design For Valve Spring Based On Multi-objective Genetic Algorithm

In the vavle-train of internal-combustion engine, the valve spring is the importantcomponent of valve assembly, the performance of valve will have a direct impact on theworking performance of the internal-combustion engine. As the development of car engine tothe miniaturized and high-speed direction, the design demands to improve the design stress ofthe valve spring on one hand, and on the other hand reducing the installation space of valvespring, to improve the comprehensive performance of the valve spring, the designer must tryhard to make each performance index of the valve spring to optimal. The optimization designof the valve spring can be belonged to multi-objective optimization problem, the traditionaldesign method exist some shortcomings in dealing with multi-objective. The multi-objectivegenetic algorithm is an excellent algorithm dealing with multi-objective optimizationproblems, it has been successfully used to solve multi-objective optimization problems ofvarious kinds of field.This thesis provides a complete description of valve springs multi-objective optimizationproblem by using multiple-objective genetic algorithm NSGA-Ⅱ which makes the valvespring has the lightest quality, minimum freedom and best defending resonance performanceas objective function and the number of workable coils of the spring n, spring diameter D andspring wire diameter d as policy variables to establish multiple-objective optimizationmathematic model. The multi-objective genetic algorithm NSGA-Ⅱ and R-NSGA-Ⅱ arerespectively used to solve the multi-objective optimization model of valve spring, and thenanalyze the results of the calculations. The valve spring’s model is analyzed based on theANSYS Workbench, and prove the exactness of the optimization result.The studies of this thesis shows that the relationship between all kinds of theperformance function can be considered based on multiple-objective genetic algorithmNSGA-Ⅱ and ideal Pareto optimization sets can be obtained. The NSGA-Ⅱ can effectivelyobtain more the optimal solution of valve springs multi-objective optimization problem, andimprove the comprehensive performance of the valve spring. The multi-objective geneticalgorithm used in this thesis is an effective multi-objective optimization tool for valve springoptimization designs.