Deep learning-driven active sheet positioning using linear actuators in laser beam butt welding of thin steel sheets

Abstract

Welding thin steel sheets in industrial applications is difficult because joint gaps occur during the process, which can lead to weld interruptions. Such welds are considered a reject and in order to avoid the weld to interrupt it is crucial to hinder the formation of joint gaps. Especially laser beam welding is affected by the emergence of gaps. Due to the narrow laser spot, product quality is highly dependent on the alignment and positioning of the sheets. This is typically done by clamping devices, which hold the workpieces in place. However, these clamps are suited for a specific workpiece geometry and require manual redesign every time the process changes. Adaptive clamping devices instead are designed to realize a time-dependent workpiece adjustment. Modeling the joint gap behavior to realize a controller for adaptive clamps can be difficult as the influence of heating, melting, and cooling on the joint gap formation is unknown and varies due to temperature dependent physical properties. Instead, the control parameters and actions can be derived using data-driven methods. In this paper, we present a novel data-driven approach how deep learning can be utilized to manipulate the sheet position during the weld with two actuators that apply force. A temporal convolution neural network (TCN) analyzes the change of the joint gap and predicts the required force to adapt the workpiece position. The developed method has been integrated into the welding process and improves the length of the average weld seam by 39.5% compared to welds without an active adjustment and 1.4% to welds that have been adapted with a constant force.