@inproceedings{3d35240c91be4761b813c8b29a829644,
title = "Data-driven Predictive Analysis for Smart Manufacturing Processes Based on a Decomposition Approach",
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.",
keywords = "Artificial Intelligence, Industrial Internet of Things, Smart Manufacturing",
author = "Mohammadhossein Ghahramani and Mengchu Zhou",
note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 2021 International Conference on Cyber-Physical Social Intelligence, ICCSI 2021 ; Conference date: 18-12-2021 Through 20-12-2021",
year = "2021",
doi = "10.1109/ICCSI53130.2021.9736216",
language = "English (US)",
series = "2021 International Conference on Cyber-Physical Social Intelligence, ICCSI 2021",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
editor = "Jiacun Wang and Ying Tang and Fei-Yue Wang",
booktitle = "2021 International Conference on Cyber-Physical Social Intelligence, ICCSI 2021",
address = "United States",
}