Article
Computer Science, Interdisciplinary Applications
Mengke Sun, Zongyan Cai, Ningning Zhao
Summary: This paper presents the application of a digital twin-based intelligent manufacturing system (DT-IMS) in smart shop floors. By establishing a high-fidelity digital twin model and using data from various information systems, real-time monitoring and scheduling of processes in smart shop floors are achieved. The results show the potential advantages of reduced complexity and uncertainty in process design, production planning and scheduling, and monitoring and control of the shop floor with the implementation of DT-IMS.
INTERNATIONAL JOURNAL OF COMPUTER INTEGRATED MANUFACTURING
(2023)
Article
Engineering, Industrial
Mingyi Guo, Xifeng Fang, Qi Wu, Shengwen Zhang, Qun Li
Summary: This paper constructs a multi-factor scheduling service system to address the problems in workshop scheduling such as delayed rescheduling response, single influencing factors, and the separation of machine tools and vehicles. By introducing digital twin theory, machine fault prediction, tool wear prediction, and product quality monitoring, the timeliness and predictability of workshop scheduling, joint scheduling of machine tools and vehicle transport tasks can be achieved.
JOURNAL OF MANUFACTURING SYSTEMS
(2023)
Article
Computer Science, Interdisciplinary Applications
Dongjie Zhang, Zhifeng Liu, Fuping Li, Yongsheng Zhao, Caixia Zhang, Xin Li, Yueze Zhang
Summary: This paper proposes a strategy for developing a digital twin polymorphic model (DTPM) for discrete manufacturing workshops to meet complex manufacturing business scenarios and improve the richness, construction efficiency, and intelligence of digital twin models.
ROBOTICS AND COMPUTER-INTEGRATED MANUFACTURING
(2023)
Article
Chemistry, Multidisciplinary
Zhifeng Liu, Fei Wang, Yueze Zhang, Jun Yan, Zhiwen Lin
Summary: This paper proposes a scene equipment saving and loading method for the digital twin workshop, which can efficiently save and load scene equipment data on the workshop. It helps workshop administrators analyze implicit problems and bottlenecks in historical manufacturing tasks, thus increasing workshop productivity.
APPLIED SCIENCES-BASEL
(2023)
Article
Automation & Control Systems
Zhongyu Zhang, Zhenjie Zhu, Jinsheng Zhang, Jingkun Wang
Summary: The paper proposes a model framework of intelligent workshop manufacturing system based on digital twin, and optimizes the production system of textile workshop using SLP and artificial bee colony algorithm, resulting in a 34.46% increase in production efficiency.
INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY
(2022)
Article
Computer Science, Interdisciplinary Applications
Jun Yan, Zhifeng Liu, Caixia Zhang, Tao Zhang, Yueze Zhang, Congbin Yang
Summary: This study addresses the impact of finite transportation conditions on scheduling in the flexible job shop scheduling problem and proposes a method to improve the scheduling results. The results show that finite transportation conditions significantly affect scheduling under different scales of scheduling problems and transportation times.
ROBOTICS AND COMPUTER-INTEGRATED MANUFACTURING
(2021)
Article
Engineering, Industrial
Litong Zhang, Yu Guo, Weiwei Qian, Weili Wang, Daoyuan Liu, Sai Liu
Summary: This paper proposes a modelling and online training method for digital twin workshop to address the difficulties in modelling, simulation, and verification. It describes a multi-level digital twin aggregate modelling method and a digital twin organization system. A spatio-temporal data model is constructed based on the data demand for digital twin aggregates. The paper also presents a training method using truncated normal distribution and a verification method based on real-virtual error for digital twin models. The effectiveness of real-time status monitoring, online model training, and production simulation is verified through a case study.
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH
(2023)
Article
Chemistry, Multidisciplinary
Jinghua Li, Wenhao Yin, Boxin Yang, Li Chen, Ruipu Dong, Yidong Chen, Hanchen Yang
Summary: In this paper, a comprehensive system architecture based on digital twin technology is proposed to improve the production efficiency and competitiveness of ocean engineering manufacturing industry. By establishing a planning model based on graph neural networks and suggesting five decision-support approaches, the problems in production planning and scheduling can be effectively addressed, achieving the goals of rapid processing and just-in-time completion. The research findings demonstrate that the proposed method outperforms traditional scheduling rules and heuristics in terms of precision rate and rapidity, and the digital twin system supports its full-scale application in future smart factories.
APPLIED SCIENCES-BASEL
(2023)
Article
Engineering, Industrial
Meng Zhang, Fei Tao, A. Y. C. Nee
Summary: This paper discusses how digital twin technology can be used for machine availability prediction, disturbance detection, and performance evaluation in dynamic scheduling of job-shop operations. A methodology for DT-enhanced dynamic scheduling is proposed, and a case study on producing hydraulic valves in a machining job-shop demonstrates the effectiveness and advantages of the approach.
JOURNAL OF MANUFACTURING SYSTEMS
(2021)
Article
Chemistry, Multidisciplinary
Wenjie Han, Jun Xu, Zheng Sun, Bin Liu, Kemu Zhang, Zhaohui Zhang, Xuesong Mei
Summary: This article proposes a digital twin-based dynamic AGV scheduling method that solves the charging and task execution problem in traditional AGV scheduling systems through the use of virtual reality and fusion features, effectively improving workshop efficiency.
APPLIED SCIENCES-BASEL
(2022)
Article
Computer Science, Interdisciplinary Applications
Yueze Zhang, Caixia Zhang, Jun Yan, Congbin Yang, Zhifeng Liu
Summary: This paper proposes a rapid construction method of equipment model (RCMEM) for a discrete manufacturing digital twin workshop system, which can meet the requirements of complex manufacturing business scenarios and improve the efficiency and quality of equipment-level digital twin model construction.
ROBOTICS AND COMPUTER-INTEGRATED MANUFACTURING
(2022)
Article
Engineering, Industrial
Tianxiang Kong, Tianliang Hu, Tingting Zhou, Yingxin Ye
Summary: This paper proposes a data construction method to provide stable and efficient data support for the applications of Digital Twin System (DTS). The framework is designed based on functional requirements and includes modules for data representation, organization, and management. A case study on cutting tool wear prediction demonstrates the feasibility and effectiveness of the proposed method.
JOURNAL OF MANUFACTURING SYSTEMS
(2021)
Article
Computer Science, Information Systems
Atif Rizwan, Rashid Ahmad, Anam Nawaz Khan, Rongxu Xu, Do Hyeun Kim
Summary: This study proposes a Digital Twin-based Federated Learning framework to monitor and control remotely deployed physical clients and their training process. The framework utilizes Raspberry Pi 4 devices as clients with limited computational capabilities and conducts experiments with structured energy consumption data. The results show minimum delay time in physical and virtual object synchronization and better performance and generalization of the global model for each client.
INTERNET OF THINGS
(2023)
Article
Engineering, Mechanical
Ke Feng, J. C. Ji, Yongchao Zhang, Qing Ni, Zheng Liu, Michael Beer
Summary: Gearbox is widely used as a power transmission system in various applications due to its compact structure, stable transmission capability, and high transmission efficiency. However, it usually operates in harsh and non-stationary working environments, causing gear surface degradation. To ensure reliable operation, it is essential to assess the progression of gear surface degradation. This paper develops a digital twin-driven intelligent health management method to monitor and assess the gear surface degradation progression, which can accurately predict the remaining useful life (RUL) of the gearbox.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2023)
Article
Engineering, Industrial
Rong Zhang, Jianhao Lv, Jinsong Bao, Yu Zheng
Summary: Under the influence of the global COVID-19 pandemic, the demand for medical equipment and epidemic prevention materials has increased significantly, but the existing production lines are not flexible and efficient enough to dynamically adapt to market demand. The human-machine collaboration system combines the advantages of humans and machines, and provides feasibility for implementing different manufacturing tasks. With dynamic adjustment of robots and operators in the production line, the flexibility of the human-machine collaborative production line can be further improved.
FLEXIBLE SERVICES AND MANUFACTURING JOURNAL
(2023)
Article
Engineering, Industrial
Xiaoliang Yan, Reed Williams, Elena Arvanitis, Shreyes Melkote
Summary: This paper extends prior work by developing a semantic segmentation approach for machinable volume decomposition using pre-trained generative process capability models, providing manufacturability feedback and labels of candidate machining operations for query 3D parts.
JOURNAL OF MANUFACTURING SYSTEMS
(2024)
Article
Engineering, Industrial
Jing Huang, Zhifen Zhang, Rui Qin, Yanlong Yu, Guangrui Wen, Wei Cheng, Xuefeng Chen
Summary: In this study, a deep learning framework that combines interpretability and feature fusion is proposed for real-time monitoring of pipeline leaks. The proposed method extracts abstract feature details of leak acoustic emission signals through multi-level dynamic receptive fields and optimizes the learning process of the network using a feature fusion module. Experimental results show that the proposed method can effectively extract distinguishing features of leak acoustic emission signals, achieving higher recognition accuracy compared to typical deep learning methods. Additionally, feature map visualization demonstrates the physical interpretability of the proposed method in abstract feature extraction.
JOURNAL OF MANUFACTURING SYSTEMS
(2024)
