Data-driven Predictive Analysis for Smart Manufacturing Processes Based on a Decomposition Approach
Document Type
Conference Proceeding
Publication Date
1-1-2021
Abstract
Smart Manufacturing refers to leveraging advanced analytics approaches and optimization techniques that are implemented in production operations. With the widespread increase in deploying various networked sensors in manufacturing processes, there is a progressive need for optimal and effective data management approaches. Embracing modern technologies to take advantage of manufacturing data allows us to overcome associated challenges, including real-time manufacturing process control and maintenance optimization. In line with this goal, a hybrid decomposition-based method including an evolutionary algorithm and an artificial neural network is proposed to make manufacturing smart. The proposed dynamic approach helps us obtain valuable insights for controlling manufacturing processes and gain perspective on various dimensions that enable manufacturers to access effective predictive technologies.
Identifier
85127599751 (Scopus)
ISBN
[9781665426213]
Publication Title
2021 International Conference on Cyber Physical Social Intelligence Iccsi 2021
External Full Text Location
https://doi.org/10.1109/ICCSI53130.2021.9736216
Recommended Citation
Ghahramani, Mohammadhossein and Zhou, Mengchu, "Data-driven Predictive Analysis for Smart Manufacturing Processes Based on a Decomposition Approach" (2021). Faculty Publications. 4456.
https://digitalcommons.njit.edu/fac_pubs/4456