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.
Highlights
Sheet metal part is widely used in aviation industry and automotive industry due to its high strength and light weight [1]
In order to constrain excessive sheet metal part deformation, Cai et al [2] put forward an “N-2-1” (N > 3) locating principle, which indicates that the “N-2-1” locating principle is more suitable for sheet metal location than “3-2-1” principle
In order to save computational time and improve the optimization efficiency, Hamedi [8] trained back propagation neural network by only a few finite element analysis (FEA) results to recognize the pattern between the clamping forces and state of contact in the workpiece-fixture system and the workpiece maximum elastic deformation
Summary
Sheet metal part is widely used in aviation industry and automotive industry due to its high strength and light weight [1]. In order to save computational time and improve the optimization efficiency, Hamedi [8] trained back propagation neural network by only a few finite element analysis (FEA) results to recognize the pattern between the clamping forces and state of contact in the workpiece-fixture system and the workpiece maximum elastic deformation. Selvakumar et al [10] used back propagation neural network to describe the function relationship of the position of the locators and clamps and the maximum workpiece deformation and combined ANN with DOE to optimize the machining fixture layout. Lu and Zhao [12] built a back propagation neural network model to predict the deformation of the sheet metal workpiece under different fixture layouts and different fixture locator errors and applied genetic algorithm to the established ANN model to find the optimal position of the fourth fixture locator based on the “N-2-1” locating principle. A case study is presented to demonstrate the proposed method, and the result shows that the method has preferable performance and higher prediction accuracy
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