电压近似熵-SVM铝合金双丝PMIG焊过程稳定性评价  

Process stability evaluation on aluminum alloy twin-wire PMIG welding by approximate entropy based SVM of voltage signal

作　　者：周晓晓[1] 王克鸿[1] 杨嘉佳[1] 黄勇[1] 周芷兰[2] 

机构地区：[1]南京理工大学材料科学与工程学院,南京210094 [2]北卡罗来纳大学教堂山分校计算机科学系,教堂山27514

出　　处：《焊接学报》2017年第3期107-111,共5页Transactions of The China Welding Institution

基　　金：总装先进制造重点资助项目(51318050117);国家国防科工局基础科研资助项目(A2620130005)

摘　　要：提出了弧压信号近似熵-支持向量机(support vector machine,SVM)算法来评价铝合金双丝脉冲熔化极惰性气体保护(pulse metal insert gas,PMIG)焊焊接过程稳定性,并经试验验证该方法具有可行性和一定的可靠性.铝合金双丝PMIG焊电流、电压信号近似熵值越大对应焊接过程越不稳定,且相比于电流近似熵值,电压近似熵值能更加明确的表现焊接过程稳定性的差异,所以选取电压近似熵值进行SVM分类.结果表明,文中数据情况下,训练数据集在20%以上时分类准确率均在90%以上,且训练数据越充足分类结果越准确.An approximate entropy(ApEn)-support vector machine(SVM) method of arc voltage was proposed to evaluate the stability of aluminum alloy twin-wire pulse metal insert gas(PMIG) welding process.A set of welding experiments were carried out and the ApEn of welding current and voltage signals was calculated.The results showed that the smaller the ApEn of current and voltage signals is the more stable,the welding process is.The application of ApEn on the welding current and the welding voltage was compared.It showed that the voltage based ApEn is sounder in measuring the stability of aluminum alloy twin-wire PMIG welding.Then a support vector machine(SVM) algorithm based on approximate entropy(ApEn) has been developed on voltage signals.And the results of the classification showed that the SVM algorithm based on ApEn can mark off the stable processes from the unstable ones.When the training data is more than 20%,the classification accuracy is more than 90%.

关 键 词：铝合金 双丝焊 稳定性 近似熵 支持向量机 

分 类 号：TG409[金属学及工艺—焊接]