A cognitive system for autonomous robotic welding
Currently, there is a high demand for autonomous industrial production systems. This paper outlines the development of a cognitive system for autonomous robotic welding. This system is based on dimensionality reduction techniques and Support Vector Machines, allowing the system to learn to separate between acceptable and unacceptable welding results within one batch, and to transfer this ability to a batch with different workpiece properties. It does not aim at a complete and general relationship between all process variables and result quantities, since it has been demonstrated that this is not necessary to reduce significantly the costs of calibrating the welding system. The main objective is to examine a cognitive system that stabilizes robotic welding processes by learning how to improve at least one process steering variable. In order to evaluate and improve the cognitive system, an extensive experimental setup is realized and described. The ability to learn and autonomously adapt to changes in workpiece properties allows the system to reduce the time an expert needs, and relaxes the requirements with respect to workpiece tolerances.