摘要
点蚀是不锈钢点焊接头最常见的失效形式之一。点蚀电位作为衡量点蚀行为的特征量,与焊接电流、焊接时间、电极压力等参数有着复杂的非线性关系。针对文献中不锈钢接头点蚀行为数据,建立随机森林模型,优化的决策树数目为1 000,通过"五折交叉验证"确定节点备选变量个数为2。预测结果表明:除29号样本预测相对误差较高外(-14.81%),剩余样本的预测结果均优于神经网络和支持向量机,相对误差的绝对值在10%以下。
Pitting is the popular failure mode of spot welding of stainless steel. The behavior of pitting could be evaluated through the eigenvalue named pitting potential,which has complicated nonlinear relations with the parameters of welding current,welding time and electrode force. The random forest is built through the specified data of the pitting behavior of stainless steel in the literature. Optimal number of decision trees are 1 000. The number of alternative variables in node is 2 by means of "five-fold cross validation". Then the parameter combination is utilized to predict the testing dataset. The results indicates that excepting a slightly higher predicted relative error of twenty-ninth sample(-14.81%),absolute value of relative error between the predicted result and the actual value of remaining samples are less than 10%,which is better than Neural Networks and support vector machine.
作者
邢易
李树枝
XING Yi;LI Shuzhi(CRRC Qingdao Sifang Co.,Ltd.,Qingdao 266111,China)
出处
《电焊机》
2020年第5期45-49,I0007,共6页
Electric Welding Machine
关键词
点蚀电位
随机森林
交叉验证
非线性
pitting potential
random forest
cross validation
nonlinear
