Multi-objective optimization of glass multi-station bending machining for smartphone curved screen

被引:0
|
作者
Wenbin He
Zhijun Chen
Wuyi Ming
Jinguang Du
Yang Cao
Jun Ma
Aiyun Wei
机构
[1] Zhengzhou University of Light Industry,Department of Electromechanical Science and Engineering
[2] Huazhong University of Science Technology,State Key Lab of Digital Manufacturing Equipment Technology, School of Mechanical Science and Engineering
[3] Guangdong Provincial Key Laboratory of Digital Manufacturing Equipment,Guangdong HUST Industrial Technology Research Institute
来源
Journal of the Brazilian Society of Mechanical Sciences and Engineering | 2019年 / 41卷
关键词
Glass multi-station bending machining (GMBM); Simulation; Multi-objective optimization; Residual stress; Shape deviation; Energy efficiency;
D O I
暂无
中图分类号
学科分类号
摘要
Glass multi-station bending machining (GMBM) is a high-precision and efficient glass processing technique for smartphone curved screen in 3C industry. In this paper, simulation model of the GMBM of smartphone curved screen was researched by using MSC Marc software. The stress relaxation and structural relaxation models of glass material were used in the numerical model to accurately predict the forming process of the glass component. The effects of process parameters of GMBM, namely heating rate (HR), holding time, bending temperature (BT), bending pressure and cooling rate (CR), on the product quality characteristics (residual stress and shape deviation) and energy efficiency were analyzed based on orthogonal experiments. It can be found that the BT, CR and HR have extremely important effects on product residual stress, shape deviation and energy efficiency. Furthermore, a multi-objective optimization method based on NSGA-III (a non-dominant sorting genetic algorithms based on reference points) was applied to efficiently solve the optimization problem between glass product quality and energy efficiency. The optimal parameter schemes with high quality and low energy efficiency were obtained by the Pareto front of multi-objective, and the average prediction errors of the numerical results by the optimized schemes are no more than 20% through confirm experiments. The optimized schemes improve the stability of the process of GMBM, which can deal with the challenge of green manufacturing.
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