Optimal sheet metal fixture locating layout by combining radial basis function neural network and bat algorithm

Zhongqi Wang,Bo Yang,Yuan Yang,Yonggang Kang

doi:10.1177/1687814016681905

Zhongqi Wang, Bo Yang + Show 2 more

Open Access

https://doi.org/10.1177/1687814016681905

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Abstract

Considering that sheet metal part has the properties of thin wall, low rigidity, easy to deform, and difficult to locate, this article proposes a new approach to optimizing sheet metal fixture locating layout by combining radial basis function neural network and bat algorithm. First, taking fixture locating layout as design variables based on the “ N-2-1” locating principle, this article generates limited training and testing sample sets by Latin hypercube sampling and finite element analysis. Second, the radial basis function neural network prediction model with the description of the nonlinear mapping relationship between the fixture locating layout and the corresponding sheet metal deformation is constructed through learning from the training sample sets. Third, bat algorithm is applied to search the optimal layout of the “ N” fixture locators for the minimum sheet metal deformation. Finally, two case studies are presented to demonstrate the optimization procedure and the effectiveness of the proposed method.

Highlights

Having the advantages of high strength and light weight, sheet metal part is widely used in various industry fields of aerospace, vehicle, and so on.1,2 sheet metal part always tends to deform at machining, assembly, and measuring stages because of its properties of thin wall, large size, and low rigidity
The three major conclusions that can be drawn from this article are the following: 1. An radial basis function neural network (RBFNN)-based prediction model describing the mapping relationship between sheet metal fixture locating layout and responding deformation is constructed
The limited training and testing sample points for RBFNN are generated by Latin hypercube sampling (LHS) and finite element analysis (FEA), and the prediction accuracy meets the need of general engineering

Summary

Introduction

Having the advantages of high strength and light weight, sheet metal part is widely used in various industry fields of aerospace, vehicle, and so on.1,2 sheet metal part always tends to deform at machining, assembly, and measuring stages because of its properties of thin wall, large size, and low rigidity. Wang et al.15 developed a radial basis function neural network (RBFNN) prediction model to assist design and optimization of sheet metal fixture locating layout by training/testing data sets selected by uniform sampling and FEA. A new approach is proposed to improve the location quality and optimization efficiency for the sheet metal fixture locating layout by integrating RBFNN and bat algorithm (BA).

Results

Conclusion