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  • 中国标准连:ISSN1005-2895
  • 续出版物号: CN 33-1180/TH
  • 主管单位:轻工业杭州机电设计研究院
  • 主办单位:轻工业杭州机电设计研究院、中国轻工机械协会、中国轻工业机械总公司
  • 社  长:刘安江
  • 主  编:黄丽珍
  • 地  址:杭州市余杭区高教路970号西溪联合科技广场4-711
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郑赣, 刘淑梅, 汪东升.基于神经网络的法兰轴冷挤压成型工艺优化[J].轻工机械,2019,37(6):16-20
基于神经网络的法兰轴冷挤压成型工艺优化
Process Optimization of Cold Extrusion Process of Flange Shaft Based on Neural Network
  
DOI:10.3969/j.issn.1005 2895.2019.06.004
中文关键词:  冷挤压成型  法兰轴  神经网络  充填不满  折叠缺陷
英文关键词:cold extrusion forming  flange shaft  neural network  unsuccessful filling  folding defects
基金项目:上海工程技术大学研究生科研创新项目(18KY0509)。
作者单位
郑赣, 刘淑梅, 汪东升 (上海工程技术大学 材料工程学院 上海201620) 
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中文摘要:
      为研究冷挤压成型工艺参数对法兰轴成型质量的影响,解决法兰轴成型时法兰盘填充不满和阶梯轴肩的折叠缺陷问题,课题组设计3因素3水平正交实验进行研究。采用3层拓扑结构,以凸模挤压速度、模具与坯料之间的摩擦因数、阶梯轴处圆角半径为输入层神经元,以折叠角和成型载荷为输出层神经元构建法兰轴冷挤压成型工艺优化神经网络模型。研究结果表明该模型的预测性能较好,精度较高。通过生产验证出优化后的工艺方案可有效解决法兰轴充填不满和折叠缺陷,为解决多变量多响应复杂的多元非线性工程问题提供了参考。
英文摘要:
      In order to study the influence of cold extrusion process parameters on the forming quality of flange shaft and solve the problems of unsatisfactory filling of flange and folding defects of stepped shaft during the forming of flange shaft, a three factors and three levels orthogonal experiment was designed and a three layers topology was used to construct.It was a bible network model for the optimization of cold extrusion process of flange shaft by taking the extrusion speed of punch, friction coefficient between mold and blank and the radius of rounded corner at the step axis as input layer neurons and folding angle and the forming load as output layer neurons. The model was trained by "normalization method", and the model was tested by prediction. The results show that the model has better prediction performance and higher accuracy. The production verification proves that the optimized process can effectively solve the problems of unsatisfactory filling of flange and folding defects, and provides a reference for solving the multivariate and multi response complex nonlinear engineering problems.
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