A review of digital twin-driven machining: From digitization to intellectualization. (April 2023)

• Record Type: Journal Article
• Authors: Liu, Shimin; Bao, Jinsong; Zheng, Pai
• Is Part Of: Journal of manufacturing systems. Volume 67(2023)
• Page Start: 361
• Page End: 378
• Publication Date: 2023-04
• Subjects: DTMaS Digital Twin- driven Machining System -- DT Digital twin -- MC Mass customization -- ZDM Zero defect manufacturing -- MaS Machining system -- CPS Cyber-physical systems -- AI Artificial intelligence -- WIP Work in progress -- AR augmented reality -- PSO Particle swarm optimization -- DTM Digital twin model -- MBD Model-based definition -- CNN Convolution neural network -- DRL Deep Reinforcement Learning -- GA Genetic algorithm -- RQ Research question -- GRNN General Regression Neural Network; Digital twin -- Intelligent machining -- Machining system -- Machining process; Manufacturing processes -- Periodicals; Production engineering -- Data processing -- Periodicals; Robots, Industrial -- Periodicals; Production, Technique de la -- Informatique -- Périodiques; Robots industriels -- Périodiques; Electronic journals; 670.42
• DOI: 10.1016/j.jmsy.2023.02.010
• Languages: English
• ISSNs: 0278-6125

Abstract

Abstract: Digital twin (DT) technology can realize the quality control of the dynamic cutting process by establishing the high-fidelity DT model, which has gradually become a hot spot in intelligent machining. Although some review articles have focused on DT, there is still a lack of clear and systematic analysis of DT-driven machining. To bridge this gap, this study conducted a state-of-the-art survey from the perspective of DT-driven machining (till 31-December-2022), covering a total of 145 selected publications. Firstly, the concepts of intelligent machining and DT-driven machining are classified. Then, the essential characteristics, operation processes, and services of the DT-driven machining system are analyzed and sorted out from three perspectives: point, line, and face. Moreover, according to the analysis of DT-driven machining, the potential research directions in the future are proposed from four aspects: high-fidelity reconstruction, collaboration, control granularity, and interactive mode. It is hoped to provide a holistic understanding of the DT-driven machining process to attract more open and in-depth discussions and research work in this field. Highlights: Proposed a systematic DTMaS diagram in regard to three key perspectives: point, line, and face. Analyzed and summarized previous works from DT model, operation process and service. Existing challenges and research directions are outlined from four aspects: model, collaboration, mechanism and display.