Quality inspection with artificial intelligence
No need to program quality, you can also teach it
It sounds so simple - with just a little effort and training, a neural network learns to identify quality requirements. Scratches, cracks, shape defects and other faults are detected reliably and without tiring. The respective products can then be sorted out before they go to the customer or are further processed.
There is no doubt that automated, image-based quality control with artificial intelligence offers many advantages over manual controls by humans or even classic machine vision approaches based on predefined rules. However, there is still a lack of experience and acceptance for AI vision technology in the industry and among users. But exactly because AI-based methods work in a completely different way than rule-based approaches, they enable the development of new tools for image processing that can be used much more intuitively. With this, human quality requirements can be transferred to AI-based image processing systems through machine learning in order to optimise and automate processes.
Read in our technical article "Quality inspection with AI vision" how different, yet advantageous and forward-looking, AI methods can be used for quality assurance. By everyone and already today!