|
Abstract：
|
|
In order to improve the optimal design efficiency of preforming for axisymmetric forgings, an automatic optimization method of axisymmetric forgings based on genetic algorithm and numerical simulation was proposed. In this automatic optimization method, CATIA and DEFORM-2D were used to work together to realize the parameterized numerical simulation of forging process, and the optimization toolbox of MATLAB was used to control the parameterized numerical simulation intelligently to realize the real-time collaborative communication. Moreover, the unit volume percentage of equivalent strain in the forgings outside the given range ［0.5,1.0］ was taken as the optimization objective, and the dimension data of the pre-forging mold was taken as the optimization design variable. Finally, for an axisymmetric forging, two schemes were designed for optimization test. The results show that the unit volume percentage of equivalent strain in the forgings within the range of ［0.5,1.0］ is increased from 85% to 95% without the folding defect.
|
|
Funds：
|
|
重庆市教委科学技术研究项目资助（KJ1603809）
|
|
AuthorIntro：
|
|
黄超群(1981-),女,硕士,副教授，E-mail：hcq_ctbi@163.com
|
|
Reference：
|
[1]Park J J, Rebelo N, Kobayashi S. A new approach to preform design in metal forming with the finite element method[J]. International Journal of Machine Tool Design & Research, 1983, 23(1):71-79.
[2]Gao T, Yang H, Liu Y L . Backward tracing simulation of precision forging process for blade based on 3D FEM[J]. Transactions of Nonferrous Metals Society of China，2006, 16(z1)：639-644.
[3]Zhao X, Zhao G, Wang G, et al. Sensitivity analysis based multiple objective preform die shape optimal design in metal forging[J]. Journal of Materials Science & Technology, 2006, 22(2):273-278.
[4]Badrinarayanan S, Zabaras N. A sensitivity analysis for the optimal design of metal-forming processes[J]. Computer Methods in Applied Mechanics and Engineering, 1996, 129(4):319-348.
[5]Cai J, Li F, Liu T. A new approach of preform design based on 3D electrostatic field simulation and geometric transformation[J]. International Journal of Advanced Manufacturing Technology, 2011, 56(5-8):579-588.
[6]朱红亮, 王广欣, 陈学文, 等. 导电杆冷锻成形数值模拟与工艺优化[J]. 锻压技术, 2019, 44(4)：16-22.
Zhu H L, Wang G X, Chen X W, et al. Numerical simulation and process optimization on cold forging of conductive rod [J]. Forging & Stamping Technology, 2019, 44(4)：16-22.
[7]汤禹成, 周雄辉, 陈军. 基于神经网络响应曲面的预锻模具形状优化与再设计方法[J]. 上海交通大学学报, 2007, 41(4): 624-628.
Tang Y C, Zhou X H, Chen J. The optimum preform design and redesign based on neural network response surface methodology [J]. Journal of Shanghai Jiaotong University, 2007, 41(4): 624-628.
[8]谢晖, 沈云飞, 王杭燕. 基于改进响应面模型的冲压回弹工艺稳健性优化[J]. 塑性工程学报, 2018,25（4）: 26-32.
Xie H, Shen Y F, Wang H Y. Robustness optimization of stamping springback based on improved response surface model[J].Journal of Plasticity Engineering, 2018,25（4）: 26-32.
[9]徐方, 董湘怀, 王新宝, 等. 矩形截面件径向锻造工艺优化[J]. 锻压技术, 2019, 44(4)：1-9.
Xu F, Dong X H, Wang X B, et al. Optimization on radial forging process of rectangular cross-section billet [J]. Forging & Stamping Technology, 2019, 44(4)：1-9.
[10]杨锋, 樊文欣, 李志伟, 等. 基于灰色神经网络模型的强力旋压连杆衬套屈服强度预测[J]. 塑性工程学报, 2018,25（4）: 212-216.
Yang F, Fan W X, Li Z W, et al. Prediction of yield strength of power spinning connecting rod bushing based on grey neural network model[J]. Journal of Plasticity Engineering, 2018,25（4）: 212-216.
[11]刘洪玉, 胡成亮, 赵震, 等. 多工位锻造工艺的多目标优化算法[J]. 塑性工程学报, 2010，17(3)：7-11.
Liu H Y, Hu C L, Zhao Z, et al. Multi-objective optimization algorithm of multi-station forging process [J]. Journal of Plasticity Engineering，2010，17(3)：7-11.
[12]贺全智, 李辉.基于灰色系统理论的筒形件变薄拉深工艺优化与预测[J].塑性工程学报,2019,26(3):57-62.
He Q Z, Li H. Optimization and prediction of ironing process for cylindrical parts based on grey system theory [J]. Journal of Plasticity Engineering，2019,26(3):57-62.
|
|
Service：
|
|
【This site has not yet opened Download Service】【Add
Favorite】
|
|
|
|
Copyright Forging & Stamping Technology.All rights reserved