Robotizing double-electrode GMAW process through learning from human welders

Gas metal arc welding (GMAW) is the most robotized arc welding process and most widely used process for wire arc additive manufacturing (WAAM). Double-electrode GMAW (DE-GMAW) is its novel modification achieved by adding a second/bypass electrode. It provides the capability to freely adjust the base metal current (heat input) without changing the wire current (mass input). However, it must be robotized in order to be adaptive to manufacturing variations and constraints. Due to the complexity of the process, we propose learning from human welders through their attempts to adjust the bypass electrode in collaboration with the robotized GMAW. To generalize human success using a follower robot/surrogate, the distance between the wire and bypass electrode is proposed as the process state to quantify human observation and operation. As such, Inertial Measurement Unit (IMU) sensors are integrated to track the wire and bypass electrode operated by both the lead robot and the human welder. To interpret human adjustments per arc observation, a Convolutional Neural Network (CNN) is employed to process arc images and calculate the distance. The automatically obtained distance labels from IMU signals are used to train the CNN; however, they are noisy and inaccurate. Thus, the CNN is finely tuned through transfer learning using manually labeled distances. With accurately automatically calculated distances from the finely tuned CNN for all the data, the demonstrations from human welders are analyzed. The generalized knowledge is implemented by a robotic surrogate, substituting for the human welder to fully automate the DE-GMAW. Experiments demonstrated the superior performance of the fully robotized, and adaptively controlled, DE-GMAW process.