AI-based modeling and data-driven evaluation for smart manufacturing processes

Smart manufacturing refers to optimization techniques that are implemented in production operations by utilizing advanced analytics approaches. With the widespread increase in deploying industrial internet of things &#x0028 IIOT &#x0029 sensors in manufacturing processes, there is a progress...

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Veröffentlicht in:IEEE/CAA journal of automatica sinica Jg. 7; H. 4; S. 1026 - 1037
Hauptverfasser: Ghahramani, Mohammadhossein, Qiao, Yan, Zhou, Meng Chu, O'Hagan, Adrian, Sweeney, James
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Piscataway Chinese Association of Automation (CAA) 01.07.2020
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Algorithms
Artificial intelligence
Classification algorithms
Data management
Evolutionary algorithms
Feature extraction
Genetic algorithms
Industrial applications
Industrial Internet of Things
Machine learning
Manufacturing
Manufacturing processes
Neural networks
Optimization
Optimization techniques
Smart manufacturing
ISSN:2329-9266, 2329-9274
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Zusammenfassung:Smart manufacturing refers to optimization techniques that are implemented in production operations by utilizing advanced analytics approaches. With the widespread increase in deploying industrial internet of things &#x0028 IIOT &#x0029 sensors in manufacturing processes, there is a progressive need for optimal and effective approaches to data management. Embracing machine learning and artificial intelligence to take advantage of manufacturing data can lead to efficient and intelligent automation. In this paper, we conduct a comprehensive analysis based on evolutionary computing and neural network algorithms toward making semiconductor manufacturing smart. We propose a dynamic algorithm for gaining useful insights about semiconductor manufacturing processes and to address various challenges. We elaborate on the utilization of a genetic algorithm and neural network to propose an intelligent feature selection algorithm. Our objective is to provide an advanced solution for controlling manufacturing processes and to gain perspective on various dimensions that enable manufacturers to access effective predictive technologies.
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ISSN:2329-9266
2329-9274
DOI:10.1109/JAS.2020.1003114