希尔伯特-黄变换
气体保护金属极电弧焊
焊接
理论(学习稳定性)
弧(几何)
材料科学
振幅
电弧焊
计算机科学
冶金
机械工程
物理
工程类
滤波器(信号处理)
光学
机器学习
计算机视觉
作者
Yong Huang,Kehong Wang,Zhilan Zhou,Xiaoxiao Zhou,Jimi Fang
标识
DOI:10.1088/1361-6501/aa5746
摘要
The arc of gas metal arc welding (GMAW) contains abundant information about its stability and droplet transition, which can be effectively characterized by extracting the arc electrical signals. In this study, ensemble empirical mode decomposition (EEMD) was used to evaluate the stability of electrical current signals. The welding electrical signals were first decomposed by EEMD, and then transformed to a Hilbert–Huang spectrum and a marginal spectrum. The marginal spectrum is an approximate distribution of amplitude with frequency of signals, and can be described by a marginal index. Analysis of various welding process parameters showed that the marginal index of current signals increased when the welding process was more stable, and vice versa. Thus EEMD combined with the marginal index can effectively uncover the stability and droplet transition of GMAW.
科研通智能强力驱动
Strongly Powered by AbleSci AI