Transactions of Materials and Heat Treatment

Title：Prediction and control on stamping multi-process precision transfer for exhaust tail pipe based on GRA-RSM

Authors： Dong Xiang Song Yanli Shen Haibo Lu Jue Li Weihao

Unit： Hubei Key Laboratory of Advanced Technology for Automotive Components Wuhan University of Technology School of Automotive Engineering Wuhan University of Technology Scien-tech Co. Ltd.

KeyWords： exhaust tail pipe springback grey correlation analysis transfer model precision prediction

ClassificationCode：TP182

year,vol(issue):pagenumber：2022,47(11):101-108

Abstract：

In order to improve the forming quality of stainless steel exhaust tail pipe, a prediction and control method of stamping multi-process precision transfer based on deformation springback was proposed. Taking the root mean square springback value at the selected point cloud as the precision evaluation index, considering the composite superposition effect of drawing and rolling processes on the workpiece springback value, the influencing factors that were closed related to the evaluation index were screened out by the grey relational analysis method, the precision transfer prediction model based on response surface method was established, and the transmission of the error caused by springback in the time domain was characterized. Then, the above model was solved by the genetic algorithm, and the optimal process parameters of drawing and rolling processes were obtained, which reduced the maximum springback value of the exhaust tail pipe after forming by 13.2% and the root mean square springback value by 12.2%. Thus, the difference of springback values at various measuring points was reduced, which effectively improved the forming precision of the exhaust tail pipe and was beneficial to improve the quality stability for the subsequent welding process.

Funds：

国家自然科学基金资助项目（51975440）；新能源汽车科学与关键技术学科创新引智基地项目（B17034）；教育部创新团队发展计划（IRT13087）；湖北省重点研发计划（2020BAB143）

作者简介：董翔(1997-)，男，硕士研究生，E-mail：Xiangdong9709@163.com；通信作者：宋燕利（1979-），男，博士，教授，E-mail：ylsong@whut.edu.cn

Reference：

[1]桂婉婷,孙玲,张民权,等.不同材料模型和测量方法对冲压回弹预测精度的影响[J].塑性工程学报,2021,28(3):34-40.

Gui W T, Sun L, Zhang M Q, et al. Influence of different material models and measurement methods on stamping springback prediction accuracy [J]. Journal of Plastic Engineering, 2021,28 (3): 34-40.

[2]Lin J P, Hou Y, Min J Y, et al. Effect of constitutive model on springback prediction of MP980 and AA6022-T4[J]. International Journal of Material Forming,2020,13(5):1-13.

[3]徐虹,刘猛,国志鹏,等.动车组变曲率L型截面铝合金门立柱拉弯精度控制[J].哈尔滨工业大学学报,2021,53(2):77-83.

Xu H, Liu M, Guo Z P, et al. Accuracy control of stretch bending for variable curvature L-section aluminum alloy door column of EMU [J]. Journal of Harbin Institute of Technology, 2021,53 (2): 77-83.

[4]Wang J, Li J T, Fu C Y, et al. Study on influencing factors of bending springback for metal fiber laminates[J]. Composite Structures,2021,261:113558.

[5]Wagoner R H, Lim H, Lee M G. Advanced Issues in springback[J]. International Journal of Plasticity,2013,45:3-20.

[6]Zhang D J, Cui Z S, Ruan X Y, et al. An analytical model for predicting springback and side wall curl of sheet after U-bending[J]. Computational Materials Science, 2007, 38(4): 707-715.

[7]Oujebbour F Z，Habbal A，Ellaia R. Optimization of stamping process parameters to predict and reduce springback and failure criterion[J]. Structural and Multidisciplinary Optimization,2015,51(2):495-514.

[8]Mrabti I E, Touache A, Hakimi A E, et al. Springback optimization of deep drawing process based on FEM-ANN-PSO strategy [J]. Structural and Multidisciplinary Optimization,2021，64:1-13.

[9]刘文杰, 雷声. 汽车纵梁成形回弹及补偿[J]. 精密成形工程, 2019, 11 (3): 127-132.

Liu W J, Lei S. Forming springback and compensation of auto girder [J]. Journal of Netshape Forming Engineering, 2019, 11 (3): 127-132.

[10]魏鑫,王雷刚,王钊,等.基于响应面法的汽车后轮罩工艺参数优化[J].锻压技术,2021,46(10):70-77.

Wei X, Wang L G, Wang Z, et al. Optimization on process parameters for automobile rear wheel cover based on response surface method [J]. Forging & Stamping Technology, 2021,46 (10): 70-77.

[11]段磊,夏磊,李庆宝,等.汽车前梁后部零件全工序成形回弹仿真及模面补偿研究[J].锻压技术,2020,45(8):62-69.

Duan L, Xia L, Li Q B, et al. Research on whole process forming springback simulation and die surface compensation for rear part of automobile front member [J]. Forging & Stamping Technology, 2020,45 (8): 62-69.

[12]耿平. 铝合金车身覆盖件全工序成形模拟与精度控制[D].武汉：武汉理工大学,2018.

Geng P. Simulation and Precision Control for the Whole Forming Process of Aluminum Alloy Automotive Body Panels [D]. Wuhan:Wuhan University of Technology, 2018.

[13]刘洪涛. 1Cr17铁素体不锈钢的成形性能研究[D].北京：北京科技大学,2018.

Liu H T. Research on Formability of 1Cr17 Ferritic Stainless Steel [D]. Beijing: University of Science and Technology Beijing, 2018.

[14]Fu J W, Cui K, Li F, et al. Texture and anisotropic mechanical proper-ties of ferritic stainless steel stabilized with Ti and Nb[J]. Materials Characterization,2020,159：110027.

[15]GB/T 228.1—2021，金属材料拉伸试验第 1 部分: 室温试验方法[S].

GB/T 228.1—2021, Metallic materials—Tensile test—Part 1: Method of test at room temperature[S].

[16]范雷,颜培,陈仕齐,等.镁合金低温切削性能及工艺参数优化[J].哈尔滨工业大学学报,2022,54(7):53-63，69.

Fan L, Yan P, Chen S Q, et al. Optimization of process parameters and performances of cryogenic cutting of magnesium alloy [J]. Journal of Harbin Institute of Technology, 2022,54(7):53-63，69.

[17]洪旗,史耀耀,路丹妮,等.基于灰色关联分析和响应面法的复合材料缠绕成型多目标工艺参数优化[J].复合材料学报,2019,36(12):2822-2832.

Hong Q, Shi Y Y, Lu D N, et al. Multi-response parameter optimization for the composite tape winding process based on grey relational analysis and response surface methodology [J]. Acta Materiae Compositae Sinica, 2019,36 (12): 2822-2832.

[18]王震虎,周巧英,刘开勇,等.基于响应面模型的白车身多目标轻量化设计[J].中国机械工程,2018,29(1):75-81.

Wang Z H, Zhou Q Y, Liu K Y, et al. Multi-objective lightweight design of BIWs based on response surface model [J]. China Mechanical Engineering, 2018,29 (1): 75-81.

[19]陈鑫,王匀,张太良,等.基于数值模拟和响应面法的CVT带轮轴终锻成形优化研究[J].塑性工程学报,2020,27(12):30-36.

Chen X, Wang Y, Zhang T L, et al. Research on optimization of final forging forming of CVT pulley shaft based on numerical simulation and response surface method [J]. Journal of Plastic Engineering, 2020,27 (12): 30-36.

Service：

【This site has not yet opened Download Service】【Add Favorite】