Supervised learning based observer for in-process tool offset estimation in robotic arc welding applications

Workpiece tolerances in manufacturing welding applications can lead to a deviation of the welding tool from the workpiece. Such a tool offset leads to reduced welding quality. This problem can be solved by measuring the exact workpiece geometry and orientation in advance of the welding process. However, measuring the workpiece geometry for each workpiece increases the manufacturing time. Therefore, this work presents a novel approach for an in-process tool offset observer. The observer model is retrieved via supervised learning methods based on real experimental welding data. The methods for extracting features from time-series data are described. A benchmark for multiple supervised learning methods and sensor types is presented. The accuracy of the trained models is tested by welding experiments. The significance of this paper is the demonstration of the feasibility of in-process tool offset estimation for robotic arc welding applications.