Analysis and development of smart production and distribution line system in smart grid based on optimization techniques involving digital twin

被引：0

作者：

Arumugam, Thangaraja ^{[1
]}

Kamble, Nitin Kundlik ^{[2
]}

Guntreddi, Venkataramana ^{[3
]}

Vishnu Sakravarthy, N. ^{[4
]}

Shanthi, S. ^{[5
]}

Ponnusamy, Sivakumar ^{[6
]}

机构：

[1] Business School, Vellore Institute of Technology, Tamilnadu, Chennai, India

[2] Department of Robotics and Automation Engineering, D Y Patil College of Engineering, Akurdi, Maharashtra, Pune,411044, India

[3] Department of Electrical, Telecommunication and Computer Engineering, School of Engineering and Applied Sciences, Kampala International University, Isakha, Uganda

[4] Department of Mechanical Engineering, Sri Eshwar College of Engineering, Tamilnadu, Coimbatore, India

[5] School of Computer Science & Engineering, Presidency University, Karnataka, Bengaluru,560064, India

[6] Department of Computer Science and Engineering, K.S.R. College of Engineering, Tiruchengode, TamilNadu, Namakkal, India

来源：

Measurement: Sensors | 2024年 / 34卷

关键词：

Assembly - Cell proliferation - Cost reduction - Decision making - Neural networks - Smart power grids;

D O I：

10.1016/j.measen.2024.101272

中图分类号：

学科分类号：

摘要：

The term Digital Twin (DT) is defined as the virtual demonstration of an object that is represented through real-time datasets. DT is done through artificial intelligence to enhance decision-making techniques. DT includes the process of simulation, amalgamation, observation, analysis, and conservation. The DT is simply the exact reproduction of the physical structures. DT is used in the identification and evaluation of problems through real-time analysis. It is important to have prior analysis and evaluation of the object before existing in the real world. These digital twins help in the manufacturing and implementation of the production line system. DT includes the production line with the station division and the hours needed for the operating conditions for the assembly process. The systems are integrated to reduce the overall cost parameter. The physical simulation model is employed to obtain higher performance with reduced cost. An artificial neural network with a genetic algorithm is used for the optimization process to achieve a production line system using digital twins. © 2024 The Authors

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