基于SAX算法的CMT增材制造缺陷在线监测

Online defect monitoring of CMT additive manufacturing based on sax algorithm

针对增材制造过程中形成的缺陷会对工件造成不可逆的影响,分析了冷金属过渡(Cold metal transfer,CMT)增材制造过程的焊接电流信号和焊接电压信号,提出了一种基于时间序列算法的CMT增材制造缺陷在线监测方法。设置不同的焊接工况,收集良好组和缺陷组的原始焊接电流和焊接电压信号,使用SAX(Symbolic aggregate approximation)算法对数据进行预处理。使用随机森林模型对数值型数据再分类,达到实时监测的效果;同时为突出SAX算法的优越性,设置对比试验组,将原始的焊接电流数据直接放入随机森林模型进行分类。结果表明,原始焊接电流组的测试集准确率为80%,SAX算法数据预处理组的测试集准确率为96%。

Aiming at the defects formed in the manufacturing process of additive,it will cause irreversible influence on the workpiece.The current signal and voltage signal of CMT additive manufacturing process were analyzed,and an online monitoring method of CMT additive manufacturing defects based on time series algorithm was proposed.Different welding conditions were set,the original current and voltage signals of good group and defective group were collected,and SAX(Symbolic aggregate approximation)algorithm was used to preprocess the data.Random forest model was used to reclassify numerical data to achieve real-time monitoring effect.At the same time,in order to highlight the superiority of SAX algorithm,a comparative test group was set up,and the original current data was directly put into the random forest model for classification.The experimental results showed that the accuracy of the test set of the original current group was 80%,and that of the SAX algorithm data preprocessing group was 96%.