Systematic generation and evaluation of synthetic production data for industry 5.0 optimization

Our research focused on analyzing and advancing information technologies to identify ecological parameters in production. The primary goals were to enhance efficiency, reduce waste, and minimize the environmental impact of manufacturing processes. By incorporating the results of the study, we observed and systematized changes occurring in the transition from Industry 4.0 to Industry 5.0. Special attention was given to studying processes and technologies related to the generation of synthetic data and analyzing the implementation of cutting-edge technologies. The research object includes new parameters introduced within the framework of Industry 5.0, encompassing automation and cognitive technologies. Our scientific interests also extended to synthetic data used in modeling various production processes, including optimizing device performance in manufacturing and forecasting abnormal situations in industrial equipment operations. The subject of the research involves algorithms for generating synthetic data and methods for validating them to ensure their statistical similarity to real-world data. During the study, we also analyzed the impact of artificial intelligence implementation on improving the efficiency and adaptability of manufacturing systems.