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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.


The flowchart of the prediction model for sheet metal fixture locating layout.
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fig4: The flowchart of the prediction model for sheet metal fixture locating layout.

Mentions: After the sample data is selected and normalized, the training and testing work for neural network can be conducted. Due to the nonlinear mapping relationship between the input and output, the initial weights play a great role in deciding whether the training work can achieve a local minimum or can converge. Therefore, evenly distributed decimal empirical value should be chose as the initial weights. Then, the above network is simulated and calculated with MATLAB, and the nonlinear mapping between the input and output is realized. The flowchart of the prediction model for sheet metal fixture locating layout is depicted in Figure 4.


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)

The flowchart of the prediction model for sheet metal fixture locating layout.
© Copyright Policy
Related In: Results  -  Collection

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

fig4: The flowchart of the prediction model for sheet metal fixture locating layout.
Mentions: After the sample data is selected and normalized, the training and testing work for neural network can be conducted. Due to the nonlinear mapping relationship between the input and output, the initial weights play a great role in deciding whether the training work can achieve a local minimum or can converge. Therefore, evenly distributed decimal empirical value should be chose as the initial weights. Then, the above network is simulated and calculated with MATLAB, and the nonlinear mapping between the input and output is realized. The flowchart of the prediction model for sheet metal fixture locating layout is depicted in Figure 4.

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.