On the Use of Arc Radiation to Detect the Quality of Gas Metal Arc Welds

Abstract

Gas metal arc (GMA) welding has over the years grown both in stature and strength to become a widely used welding process in the industries related to manufacturing. With sustained and noticeable developments in the technologies related to welding a need to automatically detect and even control the quality of welding has become both essential and desirable. Since defects present in the weld are mainly caused by an instability of the welding process, the overall quality of the welds can be detected from this instability. In order to detect the quality of the weld made by GMA welding, the arc light spectrum was collected and thoroughly analyzed and is the basis of presentation and discussion in this technical manuscript. The instability of the welding process is caused by either an improper selection and use of the welding parameters or the disturbance factors that are introduced during the welding process. Preliminary studies have shown that radiation of arc light comprised of spectral zones each having different characteristic. Some zones were abundant in the line spectra of elements (to include metal and the shielding gas argon), while some other zones were lacking in line spectra. Based on this, the spectral zones for detecting the welding process were selected. A chosen spectral zone lacking in a characteristic signal that revealed different transfer modes for the weld droplet for different arc lengths was carefully analyzed. With the introduction of disturbance factors in the welding process the resultant spectral signal of the welding arc was collected for the purpose of studying and resultant analysis. Results revealed the different disturbances to have a unique signal characteristic in the different spectral zones.

Development of Automatic GMAW Setup for Process Improvements: Experimental and Modeling Approach

“…Weld penetration monitoring was found to be better with the arc sound kurtosis followed by arc power and weld peak temperature [6]. Collecting arc light spectrum, quality of weld bead in terms of surface pores, bead bumping, and spatter were analyzed for a GMAW process [7]. In order to decide the best parametric combination for sound welding with high efficiency, optimization approaches have been adopted during pulsed metal inert gas welding using neurogenetic algorithm approach [8].…”