Knowledge-enabled digital twin for smart designing of aircraft assembly line

摘要

Purpose - The purpose of this paper is to provide a knowledge-enabled digital twin for smart design (KDT-SD) of aircraft assembly line (ML) to enhance the ML efficiency, performance and visibility. Modern MLs usually need to have capabilities such as digital-physical interaction and self-evaluation that brings significant challenges to traditional design method for AAL. The digital twin (DT) combining with reusable knowledge, as the key technologies in this framework, is introduced to promote the design process by configuring, understanding and evaluating design scheme. Design/methodology/approach - The proposed KDT-SD framework is designed with the introduction of DT and knowledge. First, dynamic design knowledge library (DDK-Lib) is established which could support the various activities of DT in the entire design process. Then, the knowledge-driven digital ML modeling method is proposed. At last, knowledge-based smart evaluation is used to understand and identify the design flaws, which could further improvement of the design scheme. Findings - By means of the KDT-SD framework proposed, it is possible to apply DT to reduce the complexity and discover design flaws in ML design. Moreover, the knowledge equips DT with the capacities of rapid modeling and smart evaluation that improve design efficiency and quality. Originality/value - The proposed KDT-SD framework can provide efficient design of ML and evaluate the design performance in advance so that the feasibility of design scheme can be improved as much as possible.