| 摘要: |
| 文中针对微点焊的特点采用电压信号监控点焊质量.焊接过程中电压通过自动数据采集系统获取.首先分析并解释了电压曲线的变化趋势,指出电压曲线的峰值就是β峰值;进而从电压曲线中提取了4个特征值用于预测熔核直径并将其作为人工神经网络的输入.预测输出的熔核直径与实测直径的误差为0.13 mm,结果表明,利用电压信号监测钛合金微电阻点焊质量是一种非常有效、经济的手段. |
| 关键词: 微电阻点焊 电压曲线 质量监控 人工神经网络 |
| DOI： |
| 分类号: |
| 基金项目:华中科技大学创新研究院技术创新基金资助项目(01-18-240036) |
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| Quality assessment using dynamic voltage characteristics in small scale resistance spot welding of titanium alloy |
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ZHAO Dawei1, WANG Xinyang2, WANG Yuanxun1, YANG Hao1, ZHANG Lei1
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1.Hubei Key Lab for Engineering Structural Analysis and Safety Assessment, Huazhong University of Science and Technology, Wuhan 430074, China;2.Nanjing Kisen International Engineering Co., Ltd, Nanjing 210036, China
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| Abstract: |
| The voltage between the electrodes in smallscale resistance spot welding (SSRSW) of titanium alloy was collected by data acquisition system. The experiments revealed that the dynamic voltage signal included a lot of welding quality information. In order to demonstrate this finding and monitor the welding quality,the back-propagation artificial neural network (ANN) was employed to forecast the nugget diameter. The maximum predicted error of ANN was about 6.5%. Adjusting and monitoring the voltage waveform could be used to forecast the formation of weld nugget and monitor the quality of welded joints. |
| Key words: small-scale resistance spot welding voltage waveform quality monitoring artificial neural network |
