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Development of a Prediction Model Based on RBF Neural Network for Sheet Metal Fixture Locating Layout Design and Optimization

By Zhongqi Wang, Bo Yang, Yonggang Kang and Yuan Yang


Fixture plays an important part in constraining excessive sheet metal part deformation at machining, assembly, and measuring stages during the whole manufacturing process. However, it is still a difficult and nontrivial task to design and optimize sheet metal fixture locating layout at present because there is always no direct and explicit expression describing sheet metal fixture locating layout and responding deformation. To that end, an RBF neural network prediction model is proposed in this paper to assist design and optimization of sheet metal fixture locating layout. The RBF neural network model is constructed by training data set selected by uniform sampling and finite element simulation analysis. Finally, a case study is conducted to verify the proposed method

Topics: Computer applications to medicine. Medical informatics, R858-859.7, Neurosciences. Biological psychiatry. Neuropsychiatry, RC321-571
Publisher: Hindawi Limited
Year: 2016
DOI identifier: 10.1155/2016/7620438
OAI identifier:
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