Deep Learning-Based Strategies for Improving Industrial Production Efficiency

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.

Download article (PDF)

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  -