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基于反向步长递减算法铝厚板预拉伸夹持区的预测方法
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  • 英文篇名:Holding area prediction of aluminum alloy thick plate for pre-stretching processes based on diminishing step algorithm with opposite direction
  • 作者:秦国华 ; 王志刚 ; 林锋 ; 叶海潮
  • 英文作者:QIN Guo-hua;WANG Zhi-gang;LIN Feng;YE Hai-chao;School of Aeronautical Manufacturing Engineering, Nanchang Hangkong University;
  • 关键词:铝合金厚板预拉伸 ; 极限下压量 ; 滑移因子 ; 最小夹持长度 ; 反向步长递减
  • 英文关键词:pre-stretching of aluminum alloy thick plate;;limited pressure;;slip factor;;minimum holding length;;diminishing step algorithm with opposite direction
  • 中文刊名:ZYXZ
  • 英文刊名:The Chinese Journal of Nonferrous Metals
  • 机构:南昌航空大学航空制造工程学院;
  • 出版日期:2017-10-15
  • 出版单位:中国有色金属学报
  • 年:2017
  • 期:v.27;No.223
  • 基金:国家自然科学基金资助项目(51465045,51165039);; 江西省自然科学基金资助项目(20161BAB206114)~~
  • 语种:中文;
  • 页:ZYXZ201710018
  • 页数:9
  • CN:10
  • ISSN:43-1238/TG
  • 分类号:151-159
摘要
预拉伸是消除铝合金板材内淬火残余应力的主要方法,但拉伸机夹钳对板材两端的夹持不仅影响着板材端部残余应力的消除效果,而且还涉及到拉伸后板材的锯切量、成材率等问题。因此,通过研究铝合金板材预拉伸本构模型、边界条件、失效准则等关键技术,建立了极限下压量和滑移因子的有限元分析方法。其次,通过构造初始夹持长度的计算函数,以一定步长正向从初始夹持长度继续取值,根据当前值与上一次取值之间滑移因子的差异,确定下一次取值的步长及其方向;若滑移因子相同则以相同步长继续正向取值,否则以递减的步长反向取值,直至步长的绝对值在阈值范围之内,构建最小夹持长度反向步长递减的确定算法,以此获得板材厚度、伸长率为输入的神经网络训练样本。借助神经网络的非线性映射能力,通过有限组的训练样本,构建了最小夹持长度的神经网络预测模型。将预测值与相应的有限元仿真值进行比较,结果表明预测误差在5%以内,进一步验证建立的工件变形预测模型具有合理性。
        Pre-stretching is the main method to eliminate the residual stress in aluminum alloy plate. But the holding of stretching machine clampers on the two ends of the plate can affect the cutting volume and yield of prestretched plate, as well as the elimination of residual stresses in end of plate. Therefore, by investigating the crucial technologies for the pre-stretching of aluminum alloy plate, including the constitutive model, boundary condition, failure criterion, as so on, the finite element analysis method is established for the limited pressure and slip factor. And then, while the function of initial holding length is constructed properly, the sum of the current holding length with a certain step can be taken as the next holding length. Thus, when the slip factors between the current holding length and the last one are calculated, the difference of the two can be judged. If the two is the same, the next holding length increases with the same step along the same direction. Otherwise, the next holding length decreases with the diminishing step along the opposite direction. The difference of the slip factor of current holding length with last holding length is iteratively validated until the absolute value of the step is not more than the given threshold value. Thus, the diminishing step algorithm with opposite direction of the minimum holding length can be suggested to obtain the neural network training samples with the thickness of the plate and the stretch rate as input. With the nonlinear mapping of neural network, a neural network prediction model of the minimum holding length is established by the finite groups of training samples. By comparing the predicted value with the corresponding finite element simulation, it shows that the prediction error is less than 5%. Obviously, the proposed diminishing step algorithm with opposite direction is reasonable to holding area prediction of aluminum alloy thick plate for pre-stretching processes.
引文
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