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BP neural network and genetic algorithm for thefilling properties optimization of crankshaft |
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| Authors: | ZHOU Jie LU Xian zheng SHU Rui zhi LING Jun |
| Affiliation: | College of Materials Science and Engineering, Chongqing University, Chongqing 400044, P.R.China, Chongqing 401321, P.R.China;College of Materials Science and Engineering, Chongqing University, Chongqing 400044, P.R.China, Chongqing 401321, P.R.China;College of Materials Science and Engineering, Chongqing University, Chongqing 400044, P.R.China, Chongqing 401321, P.R.China;Chongqing Dajiang Jiexin Inc., Chongqing 401321, P.R.China |
| 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. |
| Keywords: | crankshaft wedge flash neural network genetic algorithm |
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