INLINE QUALITY CONTROL SYSTEMS
Q&A with Daniel Greb, Head of Image Processing at Gerhard Schubert:
transport thousands of products per minute into production and packaging lines. Which biscuit or bottle a pick & place robot then picks up and places into a tray, for example, is decided within milliseconds – and right at the beginning of the process. Scanners are most often located above the conveyor belts to capture key product characteristics, ranging from colour and shape to height. Depending on the operator’s image recognition requirements, advanced 2D and 3D scanners are available today to meet their specific needs. Both types have interfaces to the
robot control system. They exchange the captured product data with the control system via real-time bus systems – thereby literally lending a helping hand to the agile machines. Communication networks such as these serve their purpose in applications where quick, reliable data transmission is of the essence. In the food industry in particular, large quantities of sensitive products require
In your opinion, what are the advantages of the synergy between vision systems and robotics? DG: The combination of both systems – precise product detection and dynamic handling – ensures that products reach the consumer intact and in the highest quality. Uniform protocols for machine-to-machine communication are fundamental to successful machine collaboration. In spite of the drive for specialisation, manufacturers should always ensure that they use established interfaces that enable stable data transfer. No one benefits from incomplete transfer between image processing and robotics.
What role does AI play in machine collaboration? DG: An increasingly important one. Especially in demanding production conditions, such as changing lighting conditions or numerous colour nuances, it is easier to train AI for these cases than to program a separate rule for each case. The variables are simply too comprehensive for this. The tog.519 cobot from Schubert, for example, achieves remarkable speeds thanks to AI-based image processing – as it can pick up products from an unsorted pile accurately and reliably at up to 90 cycles per minute. Increasingly powerful graphics
processors are making the use of AI an attractive proposition in industry.
Schubert has been working on this for some time and is integrating such graphics processing units, or GPUs for short, into new systems. An advantage here is that even potential errors can be described in advance and communicated to the systems to prepare them as comprehensively as possible for reality. However, there is still no way around rule-based algorithms, especially for clearly specifiable products such as biscuits, where unexpected changes are unlikely to occur.
Where do you see potential for optimisation – and what would it look like in practice? DG: Image processing systems provide valuable data on production quality, which could be made even more easily accessible to manufacturers in the food and other industries. I’m thinking of cloud solutions that store this data so that it can be accessed from anywhere – and no one has to stand at the machine to view it. Machine manufacturers such as Schubert could in turn use this information to train AI or readjust the settings of individual systems. However, this requires the consent of the manufacturers who use these image processing solutions. The potential of this approach hinges on the willingness to achieve the greatest possible data transparency.
NOVEMBER 2025 • KENNEDY’S CONFECTION • 17 JULY 2025
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