The Implementation Research of Data Mining Algorithms in Production Process Optimization and Management
- DOI
- 10.2991/978-94-6463-447-1_6How to use a DOI?
- Keywords
- Data mining; Production process optimization; Management decision-making
- Abstract
In response to the demands of intelligent manufacturing, this study delves into the entire production process data of engineering machinery enterprises, constructing a data-driven model for predicting and optimizing production process quality. This model integrates support vector machines, AdaBoost, and deep learning algorithms to accurately predict process states and automatically trigger optimization decisions. One month after implementing the model, quality loss time reduced by 46%, and accident response time shortened by 55% compared to the pre-implementation period. The research validates the optimization effects of data mining algorithms in the production process and lays the foundation for building a digital twin production system. Subsequent work will continue to deepen in the direction of full-process modeling and simulation optimization.
- 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 - Hongyu Li AU - Ning Cui PY - 2024 DA - 2024/07/14 TI - The Implementation Research of Data Mining Algorithms in Production Process Optimization and Management BT - Proceedings of the 2024 3rd International Conference on Engineering Management and Information Science (EMIS 2024) PB - Atlantis Press SP - 44 EP - 53 SN - 2352-538X UR - https://doi.org/10.2991/978-94-6463-447-1_6 DO - 10.2991/978-94-6463-447-1_6 ID - Li2024 ER -