共查询到18条相似文献,搜索用时 562 毫秒
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逆变电阻点焊动态参数测试系统的研究 总被引:1,自引:1,他引:0
焊接电流、电压的准确测量对于精密控制逆变电阻点焊电源和保证焊接质量有着重要的作用.采用霍尔元件及其外围电路,对逆变电阻点焊的焊接电流进行测量,并设计了电压测量电路;为了减少CPU的计算时间,设计了电压有效值处理电路.以0.4mm厚的低碳钢板作为焊接实验材料,改变焊 接试验条件,得到各种焊接焊接实验效果.利用MATLAB对电阻点焊的焊接电流和电压有效值进行计 算和分析,得出焊接过程中低碳钢的动态电阻.结果表明:动态电阻能有效地反映点焊熔核的生长过程, 并且与熔核的尺寸有一定的关系,直接影响着焊接实验效果. 相似文献
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This study focuses on weld quality evaluation by electrode voltage in small-scale resistance spot welding of titanium alloy. Voltage curve could be divided into four stages based on the variation characteristic. The single voltage peak was detected as combined effects of increasing bulk material resistivity and nugget size. Variations of voltage curve shape, voltage peak and failure load were more sensitive to welding current than electrode force. A generalised regression neural network was proposed to evaluate weld quality using features extracted from voltage signal. A discrete Hopfield neural network was also applied for electrode voltage recognition. The recognised voltage patterns were found effective in identifying different quality levels. A real-time and on-line quality monitoring system could be developed. 相似文献
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Poor quality of resistance spot welding (RSW) often causes quality issues like structural integrity and noise in the car body assembly. Research activities for reliable monitoring methods of RSW quality have therefore been extensive. So far, most of the monitoring methods found in literature are good for off-line utilization only and thus very expensive to apply. This paper introduces into a real-time and in-situ RSW quality monitoring method, which takes the input electrical impedance of the welding system as the monitoring signature. This signature is obtained by probing and processing the input voltage and current throughout the welding process. As input impedance characterizes a dynamic system, its variation with time reveals the conditions of the welding process which result in the final weld quality. By recognizing the pattern of the real part by an artificial neural network, we demonstrate that the weld quality could be classified non-destructively and automatically. Due to the fast signal collecting and processing, the quality monitoring is finished almost in real-time, i.e., classification can be completed before the next welding process is started. Another feature of the method is being in-situ because monitoring action does not jeopardize the welding operation or alter any of the welding parameters in general. 相似文献