2.5 - Controlling Laser Material Processing with Real-Time Algorithms on Cellular Neural Networks
- SENSOR+TEST Conferences 2011
2011-06-07 - 2011-06-09
- Proceedings OPTO 2011
- O2 - 3D and Calibration
- P. Strohm, A. Blug, D. Carl, H. Höfler - Fraunhofer Institute for Physical Measurement Techniques -IPM-, Freiburg (Germany), O. Krause, M. Panzner - Fraunhofer Institut -IWS-, Dresden (Germany)
- 60 - 65
Today image processing using a coaxial camera setup is used to monitor the quality of laser material processes such as laser welding, cutting, ablation or scribing. For real-time control of highly dynamic laser processes these systems are far to slow. This article proposes a sensing system for the next step: Using image based quality features in a real-time algorithm to form an instant feedback signal with up to 15 kHz in order to maintain the process in the desired state. The key component of the control system is a camera based on Cellular Neural Networks (CNN). This Single Instruction Multiple Data technology enables real time image processing by integrating processing units in every camera pixel. Moreover each of the pixel units is interconnected with its respective neighbour units which is optimal for most image processing algorithms. An integrated FPGA provides external devices with control feedback signals.
In a laser ablation application, which was developed with our partners at Fraunhofer IWS in Dresden, the CNN system is used to provide the trigger signal for a pulse laser by checking optical quality features of a workpiece. The laser pulses should ablate only the top carbonite layer. Defining the reference input variable as the percentage of ablation of the workpiece, the camera system triggers the pulse laser as long as the ablation process needs to remove the upper layer - but no longer. This is done by checking reflection and shape of the ablated underlying gold layer in real time. The image processing algorithm provides a quality rating of the current state of the workpiece and stops the laser ablation process when best quality is achieved. As a result the control algorithm decreases the pulse numbers, saves time and energy and increases the quality of the underlying layer with evaluation and control rates up to 10kHz.
In another application, which we have developed with our partners at IFSW Stuttgart and TU Dresden, the real-time algorithm on the CNN system is used to control the power of a laser welding system. By detecting the contour of the full penetration hole and using a special control algorithm the laser power is kept in the optimal range for high quality welding. Compared to conventional systems the frame rate for acquisition, evaluation and controlling rises from about 1 kHz to 14 kHz.