BP neural network and genetic algorithm for thefilling properties optimization of crankshaft
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Abstract:
The structure of wedge flash is proposed to improve the filling properties for deep cavity structure of crankshaft die with dimensional splitting mold. BP genetic algorithm is applied to optimize the structure parameters of wedge flash based on Matlab. Samples which are selected by orthogonal test are analyzed via FEM, and the minimum unfilled distance obtained are employed to conduct the BP neural network training. Then the optimum parameters with minimum unfilled distance are gained from genetic algorithm. Error between the parameters predicted and the results get from simulations is less than 5%. The productive practice indicates that the cavity is fully filled and the material utilization ratio increases from 75.7% to 81.4%, which confirms the correctness of optimization of wedge flash structure.
