Transactions of Materials and Heat Treatment

Title：Multi-objective optimization on stamping process for hemispherical part based on adaptive NSGA-II algorithm

Authors： Fan Guijie

Unit： Shandong Polytechnic

KeyWords： hemispherical part adaptive NSGA-II algorithm response surface model stamping speed blank holder force

ClassificationCode：TG386

year,vol(issue):pagenumber：2021,46(11):143-149

Abstract：

In order to reduce the stamping energy consumption of hemispherical parts and improve the quality of stamping parts, a multi-objective optimization method of stamping process based on adaptive NSGA-II algorithm was proposed. Then, the stamping process of hemispherical parts was introduced, and the stamping energy consumption and multi-objective optimization model of hemispherical parts were established. Taking the stamping speed and blank holder force as the optimization parameters, a two-factor and four-level orthogonal experiment was designed by the orthogonal experiment method. Furthermore, the energy consumption and wrinkling situation of stamping parts under different experimental conditions were obtained by software Dynaform ynaform, and the regression model between target parameters and process parameters was fitted by the second-order response surface. Finally, the adaptive variation method of crossover probability and mutation probability with individual fitness was introduced into NSGA-II algorithm, and the solution method of adaptive NSGA-II algorithm for the multi-objective optimization model was given. It is verified that the Pareto frontier solution searched by the adaptive NSGA-II algorithm is better than that of the traditional NSGA-II algorithm, and an optimization point is selected for production verification. After optimization by adaptive NSGA-II algorithm, there is no instability and wrinkling phenomenons on the surface of hemispherical parts, and the error between the average value of actual energy consumption and the optimization value is only 0.44%, which shows the accuracy of the established model and the effectiveness of the optimization method.

Funds：

山东省高等学校青创人才引育计划（2019152）

作者简介：范桂杰（1979-），女，硕士，副教授，E-mail：licanjiayou2020@163.com

Reference：

[1]苏友煌, 王军辉,冯怡爽,等.汽车内覆盖件无油冲压的数值模拟及工艺优化[J].塑性工程学报,2019,26(5):42-50.

Su Y H, Wang J H, Feng Y S, et al. Numerical simulation and process optimization of dry stamping for automotive interior cover parts [J]. Journal of Plasticity Engineering, 2019,26(5):42-50.

[2]鲜敏, 彭威,于培师,等. T形盒形件拉延冲压工艺优化[J].锻压技术,2019,44(12):20-26.

Xian M, Peng W, Yu P S, et al. Optimization on drawing and stamping process for Tshaped box [J]. Forging & Stamping Technology, 2019,44(12):20-26.

[3]王章忠, 巴志新,李琦,等. 热冲压工艺参数对22MnB5马氏体钢汽车B柱性能影响的有限元模拟[J].金属热处理,2020,45(5):221-228.

Wang Z Z, Ba Z X, Li Q, et al. Finite element simulation of influence of hot stamping process parameters on properties of 22MnB5 martensitic steel for automobile Bpillar [J]. Heat Treatment of Metals, 2020,45(5):221-228.

[4]么大锁. 汽车引擎盖外板拉延成形工艺参数优化研究[J].机电工程,2020,37(7):795-800.

Yao D S. Optimization of drawing process parameters for automobile engine hood outer plate [J]. Journal of Mechanical & Electrical Engineering, 2020,37(7):795-800.

[5]刘强, 俞国燕,梅端.基于Dynaform与RBFNSGAII算法的冲压成形工艺参数多目标优化[J].塑性工程学报,2020,27(3):16-25.

Liu Q, Yu G Y, Mei D. Multiobjective optimization of stamping forming process parameterssed on Dynaform and RBFNSGAII algorithm [J]. Journal of Plasticity Engineering, 2020,27(3):16-25.

[6]杨旭静, 冯小龙,郑娟,等. SVM和改进粒子群算法在冲压成形优化中的应用[J].汽车工程,2015,37(4):485-489.

Yang X J, Feng X L, Zheng J, et al. Applications of SVM and improved particle swarm algorithm to sheet metal forming optimization [J]. Automotive Engineering, 2015,37(4):485-489.

[7]罗厚杉, 赵程,江亮. SUS201奥氏体不锈钢表面低温盐浴硬化处理[J].材料热处理学报,2011,32(S1):158-161.

Luo H S, Zhao C, Jiang L. Salt bath hardening of SUS201 austenitic stainless steel at low temperature [J]. Transactions of Materials and Heat Treatment, 2011,32(S1):158-161.

[8]Nha VoThanh, Peter Goos, Schoen Eric D. Integer programming approaches to find rowcolumn arrangements of twolevel orthogonal experimental designs [J]. IISE Transactions,2020, 52(7):780-796.

[9]雒卫廷. 柔性机械臂的智能极值响应面优化设计方法[J].组合机床与自动化加工技术,2019，(12):40-43.

Luo W T. Intelligent extremum response surface optimization design method of flexible manipulator[J]. Modular Machine Tool & Automatic Manufacturing Technique, 2019,(12):40-43.

[10]Héctor LópezOspina, Angela AgudeloBernal, Lina ReyesMuoz, et al. Design of a location and transportation optimization model including quality of service using constrained multinomial logit[J]. Applied Mathematical Modelling,2021, 89(Part 1):428-453.

[11]季宁, 张卫星,于洋洋,等.基于最优拉丁超立方抽样方法和NSGAⅡ算法的注射成型多目标优化[J].工程塑料应用,2020,48(3):72-77.

Ji N, Zhang W X, Yu Y Y, et al. Multiobjective optimization of injection molding based on optimal latin hypercube sampling method and NSGAII algorithm[J]. Engineering Plastics Application, 2020,48(3):72-77.

[12]Chun S, Yang L. Multiobjective imperfect preventive maintenance optimisation with NSGAII[J]. International Journal of Production Research, Taylor & Francis Journals, 2020, 58(13):4033-4049.

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