The purpose of the project is to develop an intelligent self-learning system for quality control in production, which will be able to monitor the parameters of production processes, perform control procedures and data correlation from both phases, and by including a large amount of historical data, it will automatically indicate the possible causes of errors and also take appropriate action. The system will consist of two parts: an intelligent system for controlling process parameters, storing flights in a database and for analyzing this data using artificial intelligence methods; reconfigurable robotic control cells for 100% quality control on an assembly or other production line. At the level of process control, a new concept of data capture at the level of machining tools will be developed, for which the technology of micro-optical sensors will be adapted and used, which will be the case in aggressive processes, such as e.g. process of forging and subsequent treatments, enabled the capture of process parameters where they are created. The developed reconfigurable robotic inspection cell will be able to perform procedures for the final inspection of product quality and performance, which will include visual learning quality control involving active robot-human collaboration. The cell will be able to quickly adapt to changed conditions or to changed products. This is one of the key features that will enable product control even on lines that produce different types of finished products, without or with minimal human intervention. With the help of artificial intelligence algorithms, the system will automatically detect problematic relations in the event of an error or predicted a predicted problem in the future and thereby crucially helped in troubleshooting. It will cover all stages of production from the production of semi-finished products to assembly and final inspection. inspecting surfaces and performing manipulative robotic functions. For these purposes, new robotic methods will be developed and used.

The system will consist of two parts: • an intelligent system for controlling process parameters, storing them in a database and for analyzing these data using artificial intelligence methods; • reconfigurable robotic control cells for 100% quality control on an assembly or other production line. To ensure the necessary functionality of the robot cell, it will be equipped with machine vision, and at the same time, new methods of robotic learning will be developed and used, which include active cooperation between the robot and the human. With integrated machine learning algorithms, the cell will be able to quickly identify minor deviations and adapt to changed conditions or for changed products. This is one of the key features that will enable product control on our lines, which often produce very different types of finished products, without or with minimal human intervention. The developed reconfigurable robotic control cell will be able to perform procedures for final quality and product performance checks, which will include quality control with visual learning, which involves active cooperation between the robot and a human. The cell will be able to quickly adapt to changed conditions or for changed products. This is one of the key features that will enable product control even on lines that produce different types of finished products, without or with minimal human intervention. Using artificial intelligence algorithms, the system will automatically detect problematic relationships in the event of an error or predict a predicted problem in the future, thereby crucially helping to eliminate problems. It will cover all stages of production from the manufacture of semi-finished products to assembly and final control. by inspecting surfaces and performing manipulative robotic functions. For these purposes, new methods of robotic.