• ISSN 0258-2724
  • CN 51-1277/U
  • EI Compendex
  • Scopus
  • Indexed by Core Journals of China, Chinese S&T Journal Citation Reports
  • Chinese S&T Journal Citation Reports
  • Chinese Science Citation Database
Volume 29 Issue 1
Jan.  2016
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Article Contents
XIE Yanmin, HE Yujun, TIAN Yin. Optimization of Variable Blank Holder Forces in Sheet Metal Forming Based on RBF Neural Network Model[J]. Journal of Southwest Jiaotong University, 2016, 29(1): 121-127. doi: 10.3969/j.issn.0258-2724.2016.01.018
Citation: XIE Yanmin, HE Yujun, TIAN Yin. Optimization of Variable Blank Holder Forces in Sheet Metal Forming Based on RBF Neural Network Model[J]. Journal of Southwest Jiaotong University, 2016, 29(1): 121-127. doi: 10.3969/j.issn.0258-2724.2016.01.018

Optimization of Variable Blank Holder Forces in Sheet Metal Forming Based on RBF Neural Network Model

doi: 10.3969/j.issn.0258-2724.2016.01.018
  • Received Date: 16 Jun 2015
  • Publish Date: 25 Jan 2016
  • In order to solve the difficulty of training the hidden layer nodes in radial basis function (RBF) neural network during the optimization of variable blank holder forces,a RBF neural network based on the artificial immune algorithm was established by taking advantages of artificial intelligence algorithms, and then used to approximate a nonlinear function. Using both the block blank holder technology and the variable blank holder force control technology, numerical simulations were conducted in Dynaform to obtain the forming data, and an approximate model of RBF neural network was established between the variable blank holder forces and the forming quality. The approximate model was optimized by artificial immune algorithm to obtain the optimal blank holder force parameters. In addition, the method was applied to the S-rail stamping. The results show that compared with that before optimization, the maximum wrinkle amount was reduced by 89.53%, and wrinkles could be effectively controlled by the optimized variable blank holder forces.

     

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