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Application of artificial neural network and Taguchi method to preform design in metal forming considering workability |
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| Authors: | Dae-Cheol Ko Dong-Hwan Kim Byung-Min Kim |
| Affiliation: | a Department of Mechatronics, Yangsan College, Kyung-Nam, South Korea;b Department of Mechanical Design Engineering, Pusan National University, Pusan, South Korea;c ERC for Net Shape and Die Manufacturing, Pusan National University, Pusan, South Korea |
| Abstract: | This study describes a new method of perform design in muti-stage metal forming processes considering workability limited by ductile fracture. The finite element simulation combined with ductile fracture criterion has been performed in order to predict ductile fracture. The artificial neural network using the Taguchi method has been implemented for minimizing objective functions relevant to the forming process. The combinations of design parameters used in finite element simulation are selected by orthogonal array in statistical design of experiments. The orthogonal array and the result of simulation are used as train data for artificial neural networks. The cold heading process is taken as an example of designing preforms which do not form any fracture in the finished product. The results of analysis to validate the proposed design method are presented. |
| Keywords: | preform design ductile fracture artificial neural network Taguchi method |
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