基于电流多特征融合的窄间隙P-GMAW摆动电弧传感焊缝跟踪方法
Seam Tracking with an Arc Sensor in the Narrow Gap P-GMAW Process Based on the Current Multi-Feature Fusion Method
刘文吉 1朱鹏飞 1于镇洋 1杨嘉昇 1肖宇1
作者信息
- 1. 天津工业大学机械工程学院,天津 300387
- 折叠
摘要
电弧传感是实现窄间隙焊接焊缝跟踪的主要方式之一.针对电弧传感稳定性差、可靠性低的问题,提出融合摆动周期内电流信息的多个统计学特征进行偏差提取的方法,克服单一数据特征容易受到电弧稳定性影响、导致传感精度下降的问题.首先,提取电流信号的多个时域特征,计算特征矩阵与偏差矢量相关性;然后,取相关性高的特征用主成分分析的方法进行融合,取前两个主成分作为观测数据;最后,基于多分类的支持向量机模型对其进行分类试验.试验结果表明最大误差为0.2 mm,0.1 mm以内的误差占93.75%.该方法对比传统方法精度有所提升,对比神经网络方法,所用训练样本少,训练过程更加简单.
Abstract
Arc sensing is one of the important methods to track the seam in narrow gap welding.In response to the problems of poor stabil-ity and low reliability,fusing multiple statistical features of current information in the swing cycle is proposed to overcome the problem that a single data feature is easily affected by the stability of the arc.Firstly,multiple time-domain features of the current signal are extracted,and the feature matrix is calculated to correlate with the deviation vector.Then,the features with high correlation rate are fused by using the method of principal component analysis,and the first two principal components are adopted as the observation observed data.Finally,a support vector machine model based on multiple classifications is used for the classification test.The test results show that the maximum error is 0.2 mm,and the error within 0.1 mm accounts for 93.75%of the total erro.The method has improved accuracy compared with the traditional method,and the training samples used are less and the training process is simpler compared with the neural network method.
关键词
数据处理/焊缝跟踪/特征融合/电弧传感Key words
data processing/weld seam tracking/feature fusion/arc sensing引用本文复制引用
出版年
2024
国家科技期刊平台
NSTL
万方数据