标题/作者	年份
Machine learning in predictive maintenance towards sustainable smart manufacturing in industry 4.0 ZM Çınar, A Abdussalam Nuhu, Q Zeeshan, O Korhan, M Asmael, ... Sustainability 12 (19), 8211	2020
The role of ai, machine learning, and big data in digital twinning: A systematic literature review, challenges, and opportunities MM Rathore, SA Shah, D Shukla, E Bentafat, S Bakiras IEEE Access 9, 32030-32052	2021
Investigation of signal behaviors for sensor fusion with tool condition monitoring system in turning M Kuntoğlu, H Sağlam Measurement 173, 108582	2021
Industrial applications of digital twins Y Jiang, S Yin, K Li, H Luo, O Kaynak Philosophical Transactions of the Royal Society A 379 (2207), 20200360	2021
Digital twin modeling F Tao, B Xiao, Q Qi, J Cheng, P Ji Journal of Manufacturing Systems 64, 372-389	2022
A survey on AI-driven digital twins in industry 4.0: Smart manufacturing and advanced robotics Z Huang, Y Shen, J Li, M Fey, C Brecher Sensors 21 (19), 6340	2021
Data science applications for predictive maintenance and materials science in context to Industry 4.0 S Sajid, A Haleem, S Bahl, M Javaid, T Goyal, M Mittal Materials today: proceedings 45, 4898-4905	2021
A digital twin-driven production management system for production workshop J Ma, H Chen, Y Zhang, H Guo, Y Ren, R Mo, L Liu The International Journal of Advanced Manufacturing Technology 110, 1385-1397	2020
Digital twins: A survey on enabling technologies, challenges, trends and future prospects S Mihai, M Yaqoob, DV Hung, W Davis, P Towakel, M Raza, ... IEEE Communications Surveys & Tutorials	2022
Deep learning-empowered digital twin for visualized weld joint growth monitoring and penetration control Q Wang, W Jiao, YM Zhang Journal of Manufacturing Systems 57, 429-439	2020
Digitalisation and servitisation of machine tools in the era of Industry 4.0: a review C Liu, P Zheng, X Xu International journal of production research, 1-33	2021
Intelligent maintenance systems and predictive manufacturing J Lee, J Ni, J Singh, B Jiang, M Azamfar, J Feng Journal of Manufacturing Science and Engineering 142 (11), 110805	2020
Development of an edge computing-based cyber-physical machine tool J Zhang, C Deng, P Zheng, X Xu, Z Ma Robotics and Computer-Integrated Manufacturing 67, 102042	2021
Transfer learning enabled convolutional neural networks for estimating health state of cutting tools M Marei, S El Zaatari, W Li Robotics and Computer-Integrated Manufacturing 71, 102145	2021
Degradation curves integration in physics-based models: Towards the predictive maintenance of industrial robots P Aivaliotis, Z Arkouli, K Georgoulias, S Makris Robotics and computer-integrated manufacturing 71, 102177	2021
Sustainable development of smart manufacturing driven by the digital twin framework: A statistical analysis V Warke, S Kumar, A Bongale, K Kotecha Sustainability 13 (18), 10139	2021
A digital twin-driven hybrid approach for the prediction of performance degradation in transmission unit of CNC machine tool X Yang, Y Ran, G Zhang, H Wang, Z Mu, S Zhi Robotics and Computer-Integrated Manufacturing 73, 102230	2022
A novel predictive maintenance method based on deep adversarial learning in the intelligent manufacturing system C Liu, D Tang, H Zhu, Q Nie IEEE access 9, 49557-49575	2021
Designing a shape–performance integrated digital twin based on multiple models and dynamic data: a boom crane example X Lai, S Wang, Z Guo, C Zhang, W Sun, X Song Journal of Mechanical Design 143 (7), 071703	2021
Past, present, and future research of digital twin for smart manufacturing YH Son, GY Kim, HC Kim, C Jun, SD Noh Journal of Computational Design and Engineering 9 (1), 1-23	2022

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