Deep Learning Driven by Digital Twins Helps the Development and Breakthrough of Adaptive Automated Production Lines

Dezhi Niu

doi:10.47297/taposatWSP2633-456908.20250601

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Deep Learning Driven by Digital Twins Helps the Development and Breakthrough of Adaptive Automated Production Lines

6卷第1期

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Deep Learning Driven by Digital Twins Helps the Development and Breakthrough of Adaptive Automated Production Lines
Deep Learning Driven by Digital Twins Helps the Development and Breakthrough of Adaptive Automated Production Lines
Shaanxi University of Science & Technology, University Town, Weiyang District, Xi'an, Shaanxi, 710012
2025年6卷第1期页码：39-43
纸质出版日期：2025-01-21
DOI：10.47297/taposatWSP2633-456908.20250601
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(2025). Deep Learning Driven by Digital Twins Helps the Development and Breakthrough of Adaptive Automated Production Lines. 科技理论与实践（英文版）, 6(1), 39-43.

Niu Dezhi.Deep Learning Driven by Digital Twins Helps the Development and Breakthrough of Adaptive Automated Production Lines[J].Theory and Practice of Science and Technology,2025,06(01):39-43.
(2025). Deep Learning Driven by Digital Twins Helps the Development and Breakthrough of Adaptive Automated Production Lines. 科技理论与实践（英文版）, 6(1), 39-43. DOI： 10.47297/taposatWSP2633-456908.20250601.

Niu Dezhi.Deep Learning Driven by Digital Twins Helps the Development and Breakthrough of Adaptive Automated Production Lines[J].Theory and Practice of Science and Technology,2025,06(01):39-43. DOI： 10.47297/taposatWSP2633-456908.20250601.

摘要

Abstract

With the acceleration of the transformation of manufacturing to intelligence and flexibility

the integration of digital twins and deep learning technologies has brought new development opportunities for adaptive automated production lines. This paper deeply analyzes the characteristics of digital twins in constructing virtual models and mapping physical entities in real time

as well as the advantages of deep learning in data processing

pattern recognition

prediction and decision making

expounds how the collaboration of the two enables production lines to realize self-perception

dynamic optimization

and intelligent decision making

and discusses the technology application path from multiple dimensions such as accurate modeling

real-time monitoring

fault prediction

and process optimization. The challenges and future trends are analyzed to reveal the key role of this cutting-edge technology portfolio in improving the adaptive capacity of the production line and promoting high-quality development of the manufacturing industry.

关键词

Keywords

references

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