Issue |
E3S Web of Conf.
Volume 402, 2023
International Scientific Siberian Transport Forum - TransSiberia 2023
|
|
---|---|---|
Article Number | 03010 | |
Number of page(s) | 10 | |
Section | Mathematical Modeling, IT, Industrial IoT, AI, and ML | |
DOI | https://doi.org/10.1051/e3sconf/202340203010 | |
Published online | 19 July 2023 |
Modeling and assessment of the energy state of the technological system of mechanical processing when creating digital twins
1 Tashkent Institute of Irrigation and Agricultural Mechanization Engineers National Research University, Tashkent, Uzbekistan
2 Samarkand branch of the Tashkent University of Information Technologies named after Muhammad al-Khwarizmi, Samarkand, Uzbekistan
* Corresponding author: tbbai@mail.ru
An approach to the construction of digital twins of the technological system for turning low rigidity parts and the assessment of the energy state of the technological system is proposed in this article. The creating of the digital twin models by differential equations allows to excide a time-consuming procedure for training neural networks. The results obtained are transmitted to the energy state assessment block. Calculations are performed to determine the power consumption between the moving parts of the machine and the cutting process. To determine the energy, we use the Hamilton equations. The developed modeling technique and the results of computational experiments will provide an operational and resource-saving mode of production in the machining process.
© The Authors, published by EDP Sciences, 2023
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.