Automated extraction of comprehensive digital twin models for smart manufacturing systems

Manufacturing systems involve multiple, often conflicting, objectives referred to as performance indicators, including production efficiency, resource utilization, energy consumption, carbon emissions, and waste reduction, which correspond to different dimensions of the system, such as time, energy consumption, and waste generation aspects. Digital Twins have emerged as a powerful tool, integrating data-driven simulation and analysis of complex systems, such as manufacturing systems. Process Mining (PM), along with data analysis, enables the automatic discovery of executable discrete-event simulation models directly from production event logs. These data-driven models are the key to enabling near-real-time Digital Twins of discrete-event systems. Stochastic Petri Nets (SPNs) offer a robust and intuitive modeling formalism well-suited for representing the extracted models derived from PM, particularly in the context of manufacturing systems. However, standard SPNs face challenges in incorporating dimensions beyond time, such as energy consumption and waste generation. This limitation often results in suboptimal decision-making and reduced system efficiency. In this paper, we propose a Comprehensive Digital Twin (CDT) framework that employs Multi-Flow Process Mining (MFPM) to automatically extract Multidimensional Stochastic Petri Nets (MDSPNs) as underlying models of manufacturing systems. To support the modeling and simulation of extracted MDSPNs, we introduce and utilize our tool, MDPySPN. The CDT framework supports multi-objective decision-making for various performance indicators of the system. Through an illustrative case study of hot forging process chains, we showcase the development of CDT for time, energy consumption, and waste generation dimensions. We further illustrate the utilization of CDT to analyze and support decision-making to enhance the case study system according to its objectives.