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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Abstract	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.
AbstractList	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 ( IIOT ) 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. 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.
Author	Ghahramani, Mohammadhossein Qiao, Yan Sweeney, James O'Hagan, Adrian Zhou, Meng Chu
Author_xml	– sequence: 1 givenname: Mohammadhossein surname: Ghahramani fullname: Ghahramani, Mohammadhossein organization: University College Dublin, Belfield, Dublin 4, Ireland – sequence: 2 givenname: Yan surname: Qiao fullname: Qiao, Yan organization: Institute of Systems Engineering, Macau University of Science and Technology, Macau, China – sequence: 3 givenname: Meng Chu surname: Zhou fullname: Zhou, Meng Chu organization: Helen and John C. Hartmann Department of Electrical and Computer Engineering, New Jersey Institute of Technology, Newark, NJ 07102 USA – sequence: 4 givenname: Adrian surname: O'Hagan fullname: O'Hagan, Adrian organization: University College Dublin, Belfield, Dublin 4, Ireland – sequence: 5 givenname: James surname: Sweeney fullname: Sweeney, James organization: Royal College of Surgeons in Ireland, Dublin 8, Ireland
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SubjectTerms	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
Title	AI-based modeling and data-driven evaluation for smart manufacturing processes
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