Proceedings of the 2024 3rd International Conference on Engineering Management and Information Science (EMIS 2024)

Deep Learning-Based Strategies for Improving Industrial Production Efficiency

Authors
Naiwen Li1, Yuhang Luan1, *
1Liaoning Technical University, Liaoning, China
*Corresponding author. Email: 2452180028@qq.com
Corresponding Author
Yuhang Luan
Available Online 14 July 2024.
DOI
10.2991/978-94-6463-447-1_9How to use a DOI?
Keywords
Deep Learning; Industrial Production; Intelligent Optimization; Quality Inspection
Abstract

In response to the need for improved industrial production efficiency, we have developed a deep learning-based intelligent optimization solution. This solution integrates cutting-edge technologies such as computer vision and natural language processing to achieve intelligent decision-making in functions such as quality inspection, process optimization, fault prediction, and smart production scheduling. Experimental results demonstrate a significant reduction in product quality loss and equipment downtime due to anomalies, resulting in a nearly 10% increase in single-line capacity. An evaluation conducted after three months of operation indicates that the enterprise has gained over 600,000 RMB in economic benefits. This research validates the crucial value of deep learning technology in industrial intelligence and lays the foundation for continuous optimization in the future. With the accumulation of data, the system’s decision-making effectiveness continues to improve, leading to a higher level of intelligence and flexibility in the industrial production process.

Copyright
© 2024 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the 2024 3rd International Conference on Engineering Management and Information Science (EMIS 2024)
Series
Advances in Computer Science Research
Publication Date
14 July 2024
ISBN
978-94-6463-447-1
ISSN
2352-538X
DOI
10.2991/978-94-6463-447-1_9How to use a DOI?
Copyright
© 2024 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

TY  - CONF
AU  - Naiwen Li
AU  - Yuhang Luan
PY  - 2024
DA  - 2024/07/14
TI  - Deep Learning-Based Strategies for Improving Industrial Production Efficiency
BT  - Proceedings of the 2024 3rd International Conference on Engineering Management and Information Science (EMIS 2024)
PB  - Atlantis Press
SP  - 69
EP  - 77
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-447-1_9
DO  - 10.2991/978-94-6463-447-1_9
ID  - Li2024
ER  -