RSM and BPNN Modeling in Incremental Sheet Forming Process for AA5052 Sheet: Multi-Objective Optimization Using Genetic Algorithm

Xiao Xiao,Sen Yang,Young-Suk Kim,Myoung-Pyo Hong,Jin-Jae Kim

doi:10.3390/met10081003

Abstract

In this study, the response surface method (RSM), back propagation neural network (BPNN), and genetic algorithm (GA) were used for modeling and multi-objective optimization of the forming parameters of AA5052 in incremental sheet forming (ISF). The optimization objectives were maximum forming angle and minimum thickness reduction whose values vary in response to changes in production process parameters, such as the tool diameter, step depth, tool feed rate, and tool spindle speed. A Box–Behnken experimental design was used to develop an RSM and BPNN model for modeling the variations in the forming angle and thickness reduction in response to variations in process parameters. Subsequently, the RSM model was used as the fitness function for multi-objective optimization of the ISF process using the GA. The results showed that RSM effectively modeled the forming angle and thickness reduction. Furthermore, the correlation coefficients of the experimental responses and BPNN predictions of the experiment results were good with the minimum value being 0.97936. The Pareto optimal solutions for maximum forming angle and minimum thickness reduction were obtained and reported. The optimized Pareto front produced by the GA can be a rational design guide for practical applications of AA5052 in the ISF process.

Highlights

Incremental sheet forming (ISF) is a flexible sheet-forming process that has gained significant interest since the pioneering work of Iseki [1]
Were used to achievebetter the optimum parameters for maximizing the formability and minimizing roughness in the rolling, and angular direction.parameter. The results showed he finite element method the (FEM)
In this research, modeling the ISF process included for forming parameters using response surface method (RSM) and an artificial neural networks (ANN) algorithm were conducted

Summary

Introduction

Incremental sheet forming (ISF) is a flexible sheet-forming process that has gained significant interest since the pioneering work of Iseki [1]. ISF is a highly localized deformation process in which a tool is programmed to move along a certain path to create the desired part geometry. A simple ISF process to manufacture a truncated cone is depicted in Figure 1 [2]. Compared to the conventional press forming process, ISF can produce geometries of various parts directly from computer-aided design models and numerical control codes without complex tools or dies. This process saves energy, and holds great potential for rapid prototyping of small quantities of parts. It is known that ISF can significantly increase the formability of the sheet metal workpiece [3]. To enhance formability, several ISF schemes (micro ISF [4], robot-assisted ISF [5], and heat-assisted ISF [6]) have been proposed

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Methods

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Conclusion