基于声发射信号的铝合金点焊裂纹神经网络监测

Neural Network Monitoring of Aluminum Alloy Spot Welding Crack Based on Acoustic Emission Signal

铝合金热加工过程的冶金行为比较复杂,在电阻点焊快速加热和冷却条件下,极易产生裂纹缺陷。基于虚拟仪器技术,以Lab VIEW为软件平台,结合Matlab数值分析软件,构建了电阻点焊过程声发射信号采集分析及铝合金点焊裂纹监测系统。以2A12铝合金电阻点焊熔核冷却结晶过程,即点焊焊接循环维持阶段的声发射信号为研究对象,提取与声发射信号强度相关的振铃计数、能量、有效电压及5层小波分解125～250 k Hz频带能量系数4个特征参数作为输入矢量,裂纹作为输出矢量,建立3层BP神经网络铝合金点焊裂纹的监测模型,并利用测试样本对该模型进行验证。结果表明,裂纹监测的正确率达到89.1%,为监测铝合金电阻点焊裂纹提供了一种有效的方法。

The metallurgical behavior of aluminum alloy is quite complex in hot working process, especially in resistance spot welding（RSW） with the condition of rapid heating and cooling, thus the crack is one of the major defects of the aluminum alloy RSW. Based on the virtual instrument technology, a system functioning the acquisition and analysis of the acoustic emission signal and the monitoring on the crack of aluminum alloy in RSW process is established on the software Lab VIEW platform combining with Matlab. The acoustic emission signal in the cooling crystallization process of aluminum alloy 2A12 RSW, i.e., the hold time of the spot welding cycle, is chose, 4 characteristic parameters associating with the acoustic emission signal intensity, including the ring count, energy, the effective voltage and the energy coefficient from 5-layer wavelet decomposition of 125-250 k Hz band, are the input vector, and the crack is the output vector. A three-layer BP neural network monitoring model for the crack of aluminum RSW is established and verified. The verification results shows that the correct rate of the monitoring system reaches 89.1%, thus this study provides an effective method which can monitors the crack of aluminum alloy RSW.