Abstract—A collaborative optimization method for the structure parameters of combined die for bevel gear cold extrusion forming is presented in this paper, combined using finite element analysis, orthogonal experiment, neural network and genetic algorithm. Orthogonal experiment is used to design experimental schemes. Neural network is used to establish mapping relationship between die and process parameters and maximum extrusion force. Genetic algorithm is used to optimize the structure parameters. FEA evaluation validates that the collaborative optimization method presented has the same accuracy as that of conventional FEA based optimization approach, while it overcomes the deficiency of large computational resource consumption since the finite element analysis is relatively independent of optimization process and just provides training samples of neural network and evaluates the optimized results obtained. The collaborative optimization method can provide a fast and effective approach for combined die.
Index Terms—Combined die, optimum design, neural network, genetic algorithm.
The authors are with the Key Laboratory of E&M, Ministry of Education& Zhejiang Province, Zhejiang University of Technology, 310014, China(e-mail: firstname.lastname@example.org).
Cite: Qing-hua Yang, Xin Chen, Bin Meng, and Juan Pan, "Optimum Design of Combined Cold Extrusion Die for Bevel Gear," International Journal of Engineering and Technology vol. 4, no. 4, pp. 348-351, 2012.