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ZHU Wei, ZHANG Zhi-liang, DONG Xiang-huai, SHU Shi-xiang. Application of Neural Network to the Intelligentization in Sheet Deep Drawing[J]. Materials and Mechanical Engineering, 2006, 30(4): 20-22.
Citation: ZHU Wei, ZHANG Zhi-liang, DONG Xiang-huai, SHU Shi-xiang. Application of Neural Network to the Intelligentization in Sheet Deep Drawing[J]. Materials and Mechanical Engineering, 2006, 30(4): 20-22.

Application of Neural Network to the Intelligentization in Sheet Deep Drawing

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  • Received Date: March 27, 2005
  • A neural network model with three layers of BP algorithm was built with the aid of neural network tool box in software Matlab.After trained by a series group of data collections sampled from testing,this model could finally achieve the specified goal error and then was validated by another group of data collections from testing.The relative error between simulation results and test results was limited within 1%.So this neural network model could be effective in judging whether the rationality of set forming characteristic parameters was good or not in advance of actual testing,and also established a good preconditional basis for the following intelligentization in sheet deep drawing.
  • [1]
    Wang J,Xu X,Thomson P F,et al.A neural networks approach to investigating the geometrical influence on wrinkling in sheet metal forming[J].Journal of Materials Processing Technology,2000,105:215-220.
    [2]
    Manade K,Yang M,Yoshihara S.Artificial intelligent identification of process parameters and adaptive control system for deep drawing process[J].Journal of Material Processing Technoloty,1998,93(80/81):421-426.
    [3]
    罗亚军,张永清,赵军.板料拉深成形智能化控制过程中摩擦系数的识别[J].锻压技术,2001,(2):38-40.
    [4]
    汪锐,罗亚军,何丹农,等.基于模糊神经网络的压边力优化控制专家系统[J].上海交通大学学报,2001,35(3):411-415.
    [5]
    Lin J C.Using FEM and neural network prediction on hydrodynamic deep drawing of T-piece maximum length[J].Finite Elements in Analysis and Design,2003,(39):445-456.
    [6]
    赵 军,罗亚军,曹宏强.轴对称件拉深成形智能化控制过程中材料参数识别的神经网络模型设计[J].燕山大学学报,2000,24(2):95-98.
    [7]
    訾炳涛,崔建忠,巴启先,等.基于人工神经网络的凝固组织晶粒尺寸预测[J].中国有色金属学报,2001,11(3):481-484.
    [8]
    苏娟华,董企铭,刘平,等.基于人工神经网络的铜合金形变热处理工艺和性能[J].中国有色金属学报,2003,13(5):1077-1082.
    [9]
    吕冬,丁柯,何丹农,等.基于神经网络的拉深力智能化预测系统[J].中国有色金属学报,2000,10(3):420-425.
    [10]
    刘增良,刘有才.模糊逻辑与神经网络[M].北京:北京航空航天出版社,1996.
    [11]
    丛 爽.面向MATLAB工具箱的神经网络理论与应用[M].合肥:中国科技大学出版社,1998.
    [12]
    王东哲.板料拉深成形变压边力理论和实验研究[D].上海:上海交通大学,2001.

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