Development of High-Fidelity Industrial Digital Twins for Production Process Optimization and Maintenance

The research project aims to develop discrete-event and hybrid simulation models for the virtual representation of manufacturing systems, including production resources, material flows, buffers, and operational cycles. These models will support performance assessment, multi-scenario analysis, and optimization of productivity, resource utilization, robustness, and energy consumption. Research activities will also focus on mathematical, statistical, and data-driven models for condition monitoring and degradation prediction of machines and complex systems. Emphasis will be placed on integrating heterogeneous data sources and developing scalable algorithms for health state estimation and remaining useful life prediction. The project further includes the design of digital architectures for advanced after-sales services, leveraging digital twins and artificial intelligence to support operational optimization.