MIRRORING SUSTAINABILITY: HARNESSING DIGITAL TWIN WORKSHOPS FOR OPTIMAL ENERGY EFFICIENCY
Abstract
In response to the latest wave of industrial innovation and the call for sustainable and green manufacturing, China introduced "Made in China 2025" in 2015, emphasizing innovation-driven development and quality enhancement. A key component of this initiative is the comprehensive promotion of green manufacturing projects. However, the traditional manufacturing sector faces challenges related to energy consumption and environmental impact, necessitating the adoption of modern production technologies for industry optimization and advancement.
Notably, digital twin technology, initially employed for aerospace health maintenance in the United States, and the establishment of "Digital Twin Cities" in the UK for engineering solutions and low-carbon urban environments have demonstrated the transformative potential of this technology. China, in its "Fourteenth Five-Year Plan," has strategically harnessed digital twin technology to drive high-quality socio-economic development, presenting promising opportunities for technological innovation and emerging economic growth.
Keywords:
Industrial Innovation, Green Manufacturing, Digital Twin Technology, Sustainable Development, Socio-Economic Advancement.Downloads
Published
Issue
Section
How to Cite
License
Copyright (c) 2023 Xinyi Wang

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
References
Strategic Consulting Center of Chinese Academy of Engineering. Green Manufacturing [M]. Beijing: Electronic Industry Press, 2016: 2-5.
Liu Fei, Wang Qiulian, Liu Gaojun. Content system and development trend of energy efficiency research on mechanical processing system [J]. Journal of Mechanical Engineering, 2013,49 (19): 8794.
Zhang M, Zuo Y, Tao F. Equipment energy consumption management in digital twin shop-floor: A framework and potential applications[C]// 2018:1-5.
Xu W, Ji Z, Ma Y, et al. Digital twin-driven energy-efficient assessment service[M]//Digital Twin Driven Service. Academic Press, 2022: 139-171.
Tao Fei, Liu Weiran, Zhang Meng, et al. Five-dimension digital twin model and its ten applications [J]. Computer Integrated Manufacturing System, 2019, 25 (01): 1-18.
Deb K, Agrawal S, Pratap A, et al. A fast elitist non-dominated sorting genetic algorithm for multiobjective optimization: NSGA-II[C]//International conference on parallel problem solving from nature. Springer, Berlin, Heidelberg, 2000: 849-858.
Li Yunlong, Luo Guofu, Wen Xiaoyu, et al. A Flexible Job Shop Scheduling Scheme Based on Hybrid Genetic Algorithm in Cloud Manufacturing Environment [J] Journal of Light Industry, 2020, 35 (3): 99.
Chen R, Yang B, Li S, et al. A self-learning genetic algorithm based on reinforcement learning for flexible job-shop scheduling problem[J]. Computers & Industrial Engineering, 2020, 149: 106778.
Pauker F, Frühwirth T, Kittl B, et al. A systematic approach to OPC UA information model design[J]. Procedia CIRP, 2016, 57: 321-326.
Xu Bingbing. Design and Implementation of Key Modules of Data Acquisition and Monitoring System based on OPC UA [D]. Xidian University,2017.