Work

Toward an evolvable digital twin: a knowledge-driven architecture for optical manufacturing

Year: 2026

Type: article

Abstract: 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.

Source: Optics Express

DOI: 10.1364/oe.590438

Authors: Jiahao Yu, Wei Wang, Nannan Wu, Baichen Cai, Linqiang Gong, Kuo Hai

Institution: Soochow University

Language: English

Topic: Digital Transformation in Industry

Subfield: Industrial and Manufacturing Engineering

Field: Engineering

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: gold

Estimated APC paid: $2,270