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

Wang Z, Yang B, Kang Y, Yang Y - Comput Intell Neurosci (2016)

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: The Ministry of Education Key Laboratory of Contemporary Design and Integrated Manufacturing Technology, Northwestern Polytechnical University, No. 127, Youyi Road (West), Xi'an 710072, China.

ABSTRACT
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.

No MeSH data available.


Network structure of BP neural network.
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fig2: Network structure of BP neural network.

Mentions: BP neural network is a feed-forward neural network with three or more layers, including input layer, hidden layer, and output layer. It has I input nodes, J hidden nodes, and K output nodes. Since it is proved that any multivariable function can be approximated to any desired degree of accuracy with a three-layer BP neural network, a three-layer BP neural network can be used in this paper to predict the sheet metal deformation given a certain fixture locating layout. Figure 2 shows the network structure of BP neural network.


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

Wang Z, Yang B, Kang Y, Yang Y - Comput Intell Neurosci (2016)

Network structure of BP neural network.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4834399&req=5

fig2: Network structure of BP neural network.
Mentions: BP neural network is a feed-forward neural network with three or more layers, including input layer, hidden layer, and output layer. It has I input nodes, J hidden nodes, and K output nodes. Since it is proved that any multivariable function can be approximated to any desired degree of accuracy with a three-layer BP neural network, a three-layer BP neural network can be used in this paper to predict the sheet metal deformation given a certain fixture locating layout. Figure 2 shows the network structure of BP neural network.

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: The Ministry of Education Key Laboratory of Contemporary Design and Integrated Manufacturing Technology, Northwestern Polytechnical University, No. 127, Youyi Road (West), Xi'an 710072, China.

ABSTRACT
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.

No MeSH data available.