Project start: 1.10.2025.

The factories of the future will operate in environments in which machine learning is essential for automated decision-making and production management. Advanced systems rely solely on digital sensors and measuring devices to enable data processing in a digital environment. For such processes, it is extremely important that digital sensors are accurate and properly calibrated. This research deals with the digital outputs from the sensors used, the collection of data obtained by measuring a single product of a more complex surface, and their storage and analysis. The system on which the measurements will be carried out consists of certain modules that have been designed according to endorsed methodology for designing a reconfigurable measurement system. Such a system provides the possibility of improving production efficiency, all while ensuring sustainability by applying the principle of reconfiguration. Designed in such a way that it can quickly adapt to the defined requirements within a single group of objects and between different groups of objects. An appropriate system, along with its components will be established on which the quality control of products with a complex surface will be carried out, and for this purpose a reference object will be created and calibrated, which will serve to calibrate the reconfigurable measurement system. The RMS design methodology will be applied to an object with a complex surface. It will be necessary to determine individual characteristics of the controlled object, such as the surface condition, shape and required dimensions of the object. The selection of the measurement method requires the inclusion of measurement systems that can be implemented digitally, and the results will be displayed in digital form. Digitization of data obtained from the implemented measurement method is necessary for the purpose of making decisions regarding the condition of the controlled product. Data analysis will be carried out using a large number of data obtained from these systems, and the application of artificial intelligence will enable easier decision-making. The main configuration of the installed RMS will be used from previously conducted research, while the introduced upgrade will be financed by this project. The implementation of the system and the selection of the measurement method will be adjusted according to the principles of the proposed methodology from previous research, but with an emphasis on the goal of enabling the digitization of data obtained from a specific measurement.