Optical test system with hardware from mk

Optical Inspection System with Hardware from MK Intelligent software independently learns to detect, distinguish between and rate optical defects. The system described inspects small lacquered parts in a cycle. It also automates the quality assurance process in the production of small parts. The fact that the software can even see the faults is thanks to the use of the right hardware. The plant was designed by the Fraunhofer Institute for Machine Tools and Forming Technology (IWU) in Chemnitz, Germany. The frame for the cameras, light and computer as well as the conveyor technology were supplied by MK. Large quantities in the highest possible quality – individual parts, for the automotive industry especially, have to meet high production requirements. An every increasing level of automation combined with intelligent machines offers opportunities to do justice to the task. Intelligent machines. It sounds so simple. The term is often and widely used. But what does “intelligent machines” mean? A machine can only ever know what the programmer has taught it – so surely it can’t be that intelligent? Not true. Machines can learn independently, detect patterns and analyse a wide variety of data. The Fraunhofer Institute for Machine Tools and Forming Technology (IWU) in Chemnitz, Germany have now developed a system that can detect defects on black-lacquered small parts for the automotive industry. Fully automatically. The software does not just detect established defect patterns. No, the software independently learns to detect, distinguish between and rate optical defects. Intelligent Software “We used our development environment Xeidana® to develop this system,” says Alexander Pierer, research associate in the automation and monitoring department. Alexander Pierer is responsible for research and development in the area of automation and monitoring of production technologies. He took on the leadership role in this project and helped to develop the software. “One example of what the software that we implemented can do is combine the data from different or redundant sensor systems.” This sensor fusion gave them the opportunity to increase their range of identifiable defects and the rating reliability associated with that. With the aid of structure-detection techniques, the software can find complex interrelationships in data pools and identify patterns. “And that’s how the machine learns,” explains Alexander Pierer. The parts can also then be classified as “okay” or “not okay” based on “soft criteria” that are similar to human perception.

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