Towards a Deep Learning-based Online Quality Prediction System for Welding Processes

Joining technologies represent a key position in modern manufacturing. Especially fusion welding processes such as GMAW are widely used in fields such as automobile, naval, and aeronautic industries [1]. GMAW relies on electrical energy and its transformation into heat to join metallic materials and is considered one of the most efficient welding techniques for automated welding applications due to its high product yield, high reliability, and good automation capacity. Traditionally, the identification of appropriate setting parameters of the GMAW process is a challenging task that highly depends on the operator's long-time experience and expertise. Such process parameters are, for example, the welding speed, electrical cycle time, and material composition of the workpiece and welding wire. To assess the quality of the welding process, the operator primarily observes characteristic effects of the process such as the arc that builds up in the protective gas between the welding wire and the workpiece or the sound of the process that results from cyclical ejections of the liquified welding wire. The assessment of the weld quality involves the inspection of the materialistic composition and geometrical properties of the welding seam. One of the most reliable methods is to cut the welding seam and the welded workpiece transversally and inspect the resulting micro-sections.