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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 initial fixture locating layout of the aluminum alloy sheet metal part.
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fig5: The initial fixture locating layout of the aluminum alloy sheet metal part.

Mentions: In this section, the prediction model based on BP/RBF neural network for sheet metal fixture locating layout design and optimization is illustrated by an aluminum alloy sheet metal part, and its fixture locating scheme given “N = 4” is analyzed. As shown in Figure 5, the sheet metal has dimensions of 400 × 400 × 1 mm3, and the physical properties of material are listed in Table 1. The “N = 4” locating points on the primary datum plane are RP-1, RP-2, RP-3, and RP-4. The “2” locating points on the second datum plane are RP-5 and RP-6. And the “1” locating point on the third datum plane is RP-7. Set the coordinates of the fixed locating points RP-1, RP-2, RP-3, RP-5, RP-6, and RP-7 as (100, 100), (100, 300), (300, 100), (133, 0), (267, 0), and (0, 200). The locating point to be optimized is RP-4 and its coordinate is (x, y).


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 initial fixture locating layout of the aluminum alloy sheet metal part.
© Copyright Policy
Related In: Results  -  Collection

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

fig5: The initial fixture locating layout of the aluminum alloy sheet metal part.
Mentions: In this section, the prediction model based on BP/RBF neural network for sheet metal fixture locating layout design and optimization is illustrated by an aluminum alloy sheet metal part, and its fixture locating scheme given “N = 4” is analyzed. As shown in Figure 5, the sheet metal has dimensions of 400 × 400 × 1 mm3, and the physical properties of material are listed in Table 1. The “N = 4” locating points on the primary datum plane are RP-1, RP-2, RP-3, and RP-4. The “2” locating points on the second datum plane are RP-5 and RP-6. And the “1” locating point on the third datum plane is RP-7. Set the coordinates of the fixed locating points RP-1, RP-2, RP-3, RP-5, RP-6, and RP-7 as (100, 100), (100, 300), (300, 100), (133, 0), (267, 0), and (0, 200). The locating point to be optimized is RP-4 and its coordinate is (x, y).

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.