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薛海涛, 李永艳, 崔春翔, 安金龙. 基于支持向量机的铝合金点焊多类缺陷识别[J]. 焊接学报, 2008, (8): 97-100.
引用本文: 薛海涛, 李永艳, 崔春翔, 安金龙. 基于支持向量机的铝合金点焊多类缺陷识别[J]. 焊接学报, 2008, (8): 97-100.
shu
XUE Haitao, LI Yongyan, CUI Chunxiang, AN Jinlong. Identification of multiclass defects in aluminum alloy resistance spot welding based on support vector machine[J]. TRANSACTIONS OF THE CHINA WELDING INSTITUTION, 2008, (8): 97-100.
Citation: XUE Haitao, LI Yongyan, CUI Chunxiang, AN Jinlong. Identification of multiclass defects in aluminum alloy resistance spot welding based on support vector machine[J]. TRANSACTIONS OF THE CHINA WELDING INSTITUTION, 2008, (8): 97-100.
shu

基于支持向量机的铝合金点焊多类缺陷识别

Identification of multiclass defects in aluminum alloy resistance spot welding based on support vector machine

    摘要
  • 摘要: 利用从铝合金点焊过程工艺参数曲线上提取出的特征向量,建立了铝合金点焊过程喷溅缺陷和未熔合及未完全熔合缺陷的支持向量机识别模型。根据所建立的识别模型,用采集的样本数据进行了训练,并用独立的测试数据对训练的结果进行了测试。结果表明,所建立的支持向量机识别模型在给定的样本集的情况下,识别喷溅缺陷的准确率为96.7%,识别未熔合及未完全熔合缺陷的准确率为100%,利用支持向量机方法实现铝合金点焊多类缺陷的自动识别是可靠的。

     

    Abstract: A model is built to identify splash defect and incomplete fusion defect of aluminum alloy resistance spot welding based on Support Vector Machine method.The characteristic vector used in the model is extracted from process curves of aluminum alloy resistance spot welding.This model is trained and tested with different sample data.The test result shows that the accuracy rate of identifying splash defect is 96.7% and the accuracy rate of identifying incomplete fusion defect is 100% under given sample data.Therefore, it is reliable to identify multiclass defects of aluminum alloy resistance spot welding with Support Vector Machine method.

     

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