| 继电器引线的逆变电阻点焊质量智能监测 |
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| 引用本文： | 曹彪,叶玮渊,黄增好,曾敏.继电器引线的逆变电阻点焊质量智能监测[J].焊接学报,2006,27(9):47-50,54. |
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| 作者姓名： | 曹彪 叶玮渊 黄增好 曾敏 |
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| 作者单位： | 华南理工大学,机械工程学院,广州,510640 |
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| 基金项目： | 广东省科技攻关计划
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教育部留学回国人员科研启动基金 |
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| 摘 要： | 采用了人工神经网络技术对继电器制造中铜线在磷铜片上的点焊进行了质量监测.利用BP(back propagation)神经网络模型及其算法,建立以焊接电流和电极间电压作为输入参量、焊点拉剪强度作为输出参量的神经网络质量监测模型.在Matlab中对不同隐层节点和转移函数的模型进行仿真,选择合适的隐层节点数和转移函数.在逆变电阻点焊机上进行了试验验证,表明所建立的人工神经网络质量监测模型的精度能满足工程应用的要求.
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| 关 键 词： | 人工神经网络 电阻点焊 智能监测 焊点质量 |
| 文章编号： | 0253-360X（2006）09-047-05 |
| 收稿时间： | 2005-11-23 |
| 修稿时间： | 2005-11-23 |
Intelligent quality monitor of inverted resistance spot welding of wire to phosphor-copper sheet in relay manufacturing |
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| CAO Biao,YE Wei-yuan,HUANG Zeng-hao and ZENG Min.Intelligent quality monitor of inverted resistance spot welding of wire to phosphor-copper sheet in relay manufacturing[J].Transactions of The China Welding Institution,2006,27(9):47-50,54. |
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| Authors: | CAO Biao YE Wei-yuan HUANG Zeng-hao and ZENG Min |
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| Affiliation: | School of Mecharical Engineering, South China University of Technology, Guangzhou 510640, China,School of Mecharical Engineering, South China University of Technology, Guangzhou 510640, China,School of Mecharical Engineering, South China University of Technology, Guangzhou 510640, China and School of Mecharical Engineering, South China University of Technology, Guangzhou 510640, China |
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| Abstract: | The spot welding quality between copper wire and phosphor-copper sheet in relay manufacturing was monitored by using artificial neural networks(ANN) technology.Based on BP(back-propagation) ANN model and algorithm,the model took welding voltage and current as input parameters,shear load of welded joint as output parameter.Models with different hidden layer node number and transfer function were simulated in Matlab so as to select appropriate hidden layer node number and transfer function.Finally the model was tested on inverted resistance spot welder.The results show that the quality monitor model's precision by ANN estimation method is able to satisfy engineering application demand. |
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| Keywords: | artificial neural networks resistance spot welding quality monitor welding spot quality |
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