Machine intelligence for adaptable closed loop and open loop production engineering systems

This thesis investigates the application of machine learning algorithms for industrial production processes. First, the PID controller as an already existing closed loop control approach is improved. For this purpose, a neural network tunes the PID parameters, while the process is running. Second, a new architecture, consisting of several machine learning algorithms, is introduced for industrial laser welding. Following this approach, the control can be changed from open to closed loop.