Limits...
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.


“N-2-1” locating principle of sheet metal part.
© Copyright Policy
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC4834399&req=5

fig1: “N-2-1” locating principle of sheet metal part.

Mentions: So as to prevent excessive deformation and supply more reinforcements for buckling prevention at machining, assembly, and measuring stages during the whole manufacturing process, sheet metal part is always under an overconstraint condition, which is the so-called “N-2-1” locating principle. The principle considers that there are “N” (N > 3) locating points on the primary datum plane of sheet metal part and “2” and “1” on the second and third datum plane, respectively. Figure 1 shows a typical “N-2-1” principle, where 6 locators are required in order to support sheet metal on the primary datum plane to avoid excessive deflection. Meanwhile, the locator number “N,” which is always more than three, is determined by the dimensional specifications of sheet metal part. Obviously, the key problem of locating layout designing based on “N-2-1” principle is how to determine the number and position of “N,” that is, the fixture locating layout.


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)

“N-2-1” locating principle of sheet metal part.
© Copyright Policy
Related In: Results  -  Collection

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

fig1: “N-2-1” locating principle of sheet metal part.
Mentions: So as to prevent excessive deformation and supply more reinforcements for buckling prevention at machining, assembly, and measuring stages during the whole manufacturing process, sheet metal part is always under an overconstraint condition, which is the so-called “N-2-1” locating principle. The principle considers that there are “N” (N > 3) locating points on the primary datum plane of sheet metal part and “2” and “1” on the second and third datum plane, respectively. Figure 1 shows a typical “N-2-1” principle, where 6 locators are required in order to support sheet metal on the primary datum plane to avoid excessive deflection. Meanwhile, the locator number “N,” which is always more than three, is determined by the dimensional specifications of sheet metal part. Obviously, the key problem of locating layout designing based on “N-2-1” principle is how to determine the number and position of “N,” that is, the fixture locating layout.

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.