Closed loop control of laser welding processes using cellular neural network cameras - experimental results

Today, image processing using coaxial camera setups is used to monitor the quality of laser material processes such as laser welding, cutting or ablation. This article shows the potentials of a sensing system for the next step: A closed loop control of a full penetration keyhole welding process. With Cellular Neural Networks (CNN) it is possible to integrate processor elements in the electronic circuitry of CMOS cameras resulting in a Single-Instruction-Multiple-Data (SIMD)-architecture on the camera chip itself. Such pixel parallel systems provide extremely fast real-time image processing. This allows it to employ algorithms for image processing which are widely independent of the exact process parameters, reducing the adaption effort for different applications, compared to photo diode systems. A closed loop control system was implemented into a commercially available laser welding machine. This system uses a CNN based camera surveying the contour of the full penetration hole with a control frequency of up to 14 kHz for linear weldings. As a result the system reaches and holds the full penetration state automatically. A more detailed description of the control system and the used algorithms is given in [1]. This paper presents achievable welding results in scanner-based remote welding processes, with a new direction independent algorithm. To demonstrate the capabilities of the closed loop control, welding experiments with changing process conditions were carried out.