Visual inspection system hardware configuration – the last nail preventing the inspection systems to be truly purpose-flexible and thus ready for the implementation as a part of Industry 4.0 process chain. Production lines are more and more versatile, and products are changing rapidly, confronting inspection systems with more complex surfaces and materials. Every step of the production is controlled and digitalized to be as flexible as possible. And yet, when it comes to inspection, months of pre study are required, and no off-the shelf solution is available which can be easily adapted to different use cases and surfaces of different complexity.
The Virtual Image Processing research group of our institute is set on changing the paradigm by developing a modular framework fully capable of planning the acquisition requirements to completely inspect any given product. Using computer vision, computer graphics, machine learning and robotics it is possible to develop a framework offering tools for design optimization, allowing the assumption of a flexible image acquisition setup. Currently, very little or no research is focused on inspection system design and optimization.
A virtual image processing framework can overcome this gap, by thoroughly testing the acquisition hardware of choice and simulating the result. Most importantly, it makes optimization of component positioning possible, without requiring the engineer to remount the equipment repeatedly. Furthermore, computer vision algorithms can be developed and tested on simulated images, along with the acquired ones, overcoming a frequent problem of defect sample acquisition. Such problems are often found in industries where defects occur rarely but are critical when they do – airplane blisks (Blade Integrated Disk) and car brakes are two examples.