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How AI Vision inspection has evolved By Transfer Multisort Elektronik (TME)


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utomatic quality control was once associated with costly smart cameras and elaborate vision systems. Today, the development of artificial intelligence and compact embedded platforms means that many AI Vision Inspection applications can be implemented much more simply. Imagine a production line where a new part appears every few seconds. In the case of mass production, manual inspection of each element would not only be time- consuming but also uneconomical. For this reason, modern manufacturing plants are increasingly using automated quality control systems that perform inspections of each product in a repeatable and equally accurate manner. The role of the operator does not disappear – it changes its nature. Instead of checking all the details, a person usually only verifies those products that have been previously marked by the system as potentially non-compliant. This approach increases the efficiency of the process and allows focusing on cases requiring actual assessment.


In many production processes, a slight deviation from the norm – a poorly executed print, missing an element, improper colour, or damaged surface – can cause the finished product to fail quality standards. Over the years, such tasks were entrusted to employees or specialised vision systems based on smart cameras. Both solutions have their advantages, but each also has certain limitations. Manual control is time- consuming and prone to errors due to operator fatigue, while professional vision systems often require significant financial outlays and specialised knowledge during implementation.


Is there an intermediate solution? Traditional vision systems rely on a set of predefined rules. The software analyses the image, measures distances, detects edges, or compares shapes with a stored pattern. This approach works in many applications, but becomes less effective when the controlled products differ from each other or when the number of possible variants is very large.


Artificial intelligence changes the way such systems operate. Instead of rigidly programmed rules, a machine learning model is used, which learns to recognise the characteristic features of the product


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based on provided examples. After proper training, it can classify images, detect inconsistencies, or identify specific objects with high efficiency.


As a result, the system does not need to receive a detailed description of each possible defect. In many cases, it is enough to teach it what a correct product looks like or prepare a set of example photos showing both correct and defective elements. To make this possible, the model must be trained in advance on a properly prepared set of photos showing both correct and defective products. Based on this, it learns to recognise the characteristic features of individual classes and detect deviations from the pattern.


AI Vision Inspection systems find application in many industries. They are most commonly used for: • • • • • • •


Detecting missing elements Assembly correctness control


Quality checking of prints and markings Colour recognition Product classification


Detecting surface damages


Verifying the presence of labels or codes Importantly, similar solutions do not have to be implemented across the entire production line at once. More and more often, the first step is to build a small test station that allows checking


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the effectiveness of the AI model in real conditions.


Although the term AI Vision may sound complicated, the basic principle of operation of such systems is relatively simple. The camera captures an image of the product, and then a specially trained model analyses it and compares it with patterns recorded during the learning process. The biggest advantage of this approach is the ability to analyse the image in real time and maintain the same accuracy regardless of the number of products checked.


Like vibration monitoring, the vision system also performs several subsequent stages of data processing. The typical process looks like this: Camera ➞ Image Processing ➞ Machine Learning Model ➞ Classification ➞ Decision Increasingly, analysis is performed locally, without the need to send each frame of the image to a server. This reduces delays, decreases network load, and allows for faster response to detected irregularities. Just a few years ago, automatic quality control was a solution associated mainly with costly vision systems. The development of machine learning models and compact embedded platforms, however, has made building your own AI Vision Inspection station much more accessible.


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