铝合金直流点焊神经网络建模

采用三相次级整流点焊机和动态参数检测系统，研究了硬铝合金ＬＹ１２直流点焊焊接性；在大量工艺试验基础上，运用神经网络方法，构造了一种离散型人工神经网络点焊质量预测与评估模型。研究结果表明，对点焊焊接电流、极间电压和焊点剪切力等预测模型的输入、输出参数进行离散化处理，可以实现输入向量空间到输出向量空间的映射，构造的预测模型具有良好的有效性和容错性，适用于铝合金直流点焊质量的预测和评估，为最终实现电阻点焊的智能制造奠定了基础。

Neural Network Model for DC Spot Welding of Aluminum-alloy

The weldability of alumimum -- alloy 508 for DC spot welding is studied by use of DC spot welder and dynamic parameter testing system. On the basis of technical tesing, a discrete ANN model for quality Prediction and evaluation of spot welding is built. The results show that the reflection from input vector space to output vector space could be realized by discrete processing the input and putput parameter of Prediction model, such as welding current,voltage between electrodes and shearing strength. The prediction model has good ability of reliability and fault -- tolerance. It is adapted to predict and evaluate weld quality for DC spot welding of aluminum,and can be used to realize the intelligent manufacture of resistance spot welding.