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Digital twins demonstrate significant potential for addressing optical manufacturing challenges associated with high precision demands and empirical dependency, yet existing models lack dynamic and deep knowledge integration. This paper proposes a plant-growth-inspired bionic digital twin model featuring a multi-vascular knowledge network for computable causal reasoning and a dual-loop (growth and metabolic) cognitive architecture to support knowledge evolution and real-time decision-making. Validated through continuous polishing cases, the model enhances surface form error convergence, parameter optimization, and sustained cognitive adaptability through continuous knowledge updates. This research provides a pathway toward adaptive, self-evolving digital twins for intelligent optical manufacturing applications.
Yu et al. (Thu,) studied this question.