Multi-Objective Optimization of Spring Diaphragm Clutch on Automobile Based on the Non-Dominated Sorting Genetic Algorithm (NSGA-II)

To solve the problem that the weight coefficients of the objective function in the traditional optimization of diaphragm spring, it usually depends on experiences. A new optimization target function is proposed .The new function takes the minimum of average compress force changing of the spring and the minimum force of the separation as total objectives. Based on the optimization function, the result of the clutch diaphragm spring in a car is analyzed by the non-dominated sorting genetic algorithm (NSGA-II) and the solution set of Pareto is abstained. The results show that the pressing force of the diaphragm spring by the new algorithm is improved by 4.09%, which has better stability and steering portability. The problem of the weight coefficient in the traditional empirical design is solved and the method proves to be correct and effective.