An adaptive evolutionary framework for the decision-making models of digital twin machining system

Digital twin technology is the core promoter of intelligent autonomous machining systems, which has been gradually explored and applied in the machining field. The digital twin system can observe, analyze, and control the machining process in real-time by creating high fidelity virtual entity of the physical entity. However, the current digital twin system is usually customized for specific scenarios, which lacks sufficient robustness. Remodeling may lead to poor modeling effects and low modeling efficiency due to insufficient data. This paper explores an adaptive evolution mechanism of the digital twin decision-making model through incremental learning and transfer learning. Besides, the specific implementation method is concluded.