Prediction of residual stresses in gas arc welding by back propagation neural network

M. Ahmadzadeh; A. Hoseini Fard; B. Saranjam; H.R. Salimi

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Prediction of residual stresses in gas arc welding by back propagation neural network

机译：反向传播神经网络预测电弧焊的残余应力

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This paper presents the development of a back propagation neural network model for the prediction of maximum residual stresses produced in gas metal arc welding process. The model is based on results obtained from finite element models. The thickness of the plate, electrode size, welding speed, current/ voltage intensity have been considered as the input parameters and the maximum residual stresses due to welding as output parameters in the development of the model. The Levenberg-Marquardt method as a feed forward back propagation method was used in this investigation. The neural network predictions were then compared with the finite element results for accuracy, and the comparison showed that the results obtained from neural network model were sufficiently accurate in predicting the residual stresses.

机译：本文介绍了一种用于预测气体保护金属电弧焊过程中产生的最大残余应力的反向传播神经网络模型的开发。该模型基于从有限元模型获得的结果。在模型开发中，已将板的厚度，电极尺寸，焊接速度，电流/电压强度视为输入参数，并将因焊接而产生的最大残余应力作为输出参数。这项研究使用Levenberg-Marquardt方法作为前馈传播方法。然后将神经网络预测结果与有限元结果进行比较，以确保准确性，并且比较结果表明，从神经网络模型获得的结果在预测残余应力方面足够准确。

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来源
《NDT & E international》 |2012年第2012期|p.136-143|共8页
作者
M. Ahmadzadeh; A. Hoseini Fard; B. Saranjam; H.R. Salimi;
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作者单位

Young Researchers Club, Shiraz Branch, Islamic Azad University, Shiraz, Iran;

Young Researchers Club, Shiraz Branch, Islamic Azad University, Shiraz, Iran;

Mr Naval Research Center, Shiraz, Iran;

Young Researchers Club, Shiraz Branch, Islamic Azad University, Shiraz, Iran;

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收录信息美国《科学引文索引》(SCI);美国《工程索引》(EI);
原文格式 PDF
正文语种 eng
中图分类
关键词
finite element model; residual stresses; artificial neural networks; feed forward back propagation;

机译：有限元模型残余应力;人工神经网络;前馈回传;

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Prediction of residual stresses in gas arc welding by back propagation neural network

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