A novel bionic decision-making mechanism for digital twin-based manufacturing system

Abstract

As an innovative smart manufacturing system, the virtual entities-based decision-making process is the most typical difference between the digital twin-based manufacturing system (DTMS) and other smart manufacturing systems. Therefore, the accuracy of virtual entity-driven decision-making is the key to affecting the system reliability of the DTMS. Normally, the manufacturing process is often accompanied by complex state changes, which are collected by the perception module of the DTMS in the form of high-dimensional information. Then, the decision-making model needs to respond to these state changes in real-time and give reasonable decision results back to physical space, which has become an important scientific issue of DTMS. To fill this gap, a novel bionic decision-making mechanism for DTMS is put forward by introducing the biological sequential learning mechanism into the decision-making process. Subsequently, the systematic decision-making process imitates biological instinct and learning behavior mechanisms to explore the short-term and long-term process of decision-making. The bionic decision-making mode formed by combining the above two modes provides adaptive decision-making in different scenarios. It is believed that the bionic decision-making mechanism can help to quickly and accurately give decision-making feedback to guide on-site manufacturing and ensure product quality and manufacturing efficiency.