In-Process Workpiece Geometry Estimation for Robotic Arc Welding based on Supervised Learning for Multi-Sensor Inputs

Abstract

Due to manufacturing tolerances, the geometry parameters of workpieces are not constant in industrial welding applications. Today, this problem is addressed by either accepting fluctuating part quality or by measuring the geometry and adjusting the configuration of the robot and process controller for each individual part. However, measuring the geometry requires additional manufacturing time or reduces the accessibility of the welding tool. This paper presents an approach for estimating the workpiece geometry based on process data during the welding process, which eliminates the need of measuring the geometry in advance, yet provides the same accessibility of the welding tool. For this approach, a data set is generated by conducting welding experiments with varying part geometries. Welding current, welding voltage and acoustic emissions are recorded. Three supervised learning techniques, which are Support Vector Machines, Random Forest Decision Trees and k-Nearest-Neighbors, are used to train models for estimating the geometry. The results show, that the trained models successfully estimate the part geometry with over 90% accuracy.