AI-Based Modeling and Data-Driven Evaluation for Smart Manufacturing Processes

Mohammadhossein Ghahramani; Yan Qiao; MengChu Zhou; Adrian O’Hagan; James Sweeney

doi:10.1109/JAS.2020.1003114

Volume 7 Issue 4

Jun. 2020

IEEE/CAA Journal of Automatica Sinica

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Article Navigation > IEEE/CAA Journal of Automatica Sinica > 2020 > 7(4): 1026-1037

Mohammadhossein Ghahramani, Yan Qiao, MengChu Zhou, Adrian O’Hagan and James Sweeney, "AI-Based Modeling and Data-Driven Evaluation for Smart Manufacturing Processes," IEEE/CAA J. Autom. Sinica, vol. 7, no. 4, pp. 1026-1037, July 2020. doi: 10.1109/JAS.2020.1003114

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Mohammadhossein Ghahramani, Yan Qiao, MengChu Zhou, Adrian O’Hagan and James Sweeney, "AI-Based Modeling and Data-Driven Evaluation for Smart Manufacturing Processes," IEEE/CAA J. Autom. Sinica, vol. 7, no. 4, pp. 1026-1037, July 2020. doi: 10.1109/JAS.2020.1003114

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AI-Based Modeling and Data-Driven Evaluation for Smart Manufacturing Processes

doi: 10.1109/JAS.2020.1003114

Funds: This work was supported in part by the Science and Technology development fund (FDCT) of Macau (011/2017/A), and the National Natural Science Foundation of China (61803397)

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Author Bio:
Mohammadhossein Ghahramani (S’15–M’18) obtained the B.S. and M.S. degrees in information technology engineering from Amirkabir University of Technology-Tehran Polytechnic, Iran, and the Ph.D. degree in computer technology and application from Macau University of Science and Technology in 2018. He was a Technical Manager and Senior Data Analyst of the Information Centre of Institute for Research in Fundamental Sciences from 2008 to 2014. He is currently a Post-Doctoral Research Fellow at University College Dublin (UCD), Ireland. He is also a Member of the Insight Centre for Data Analytics at UCD. His research interests include smart cities, machine learning, artificial intelligence, internet of things, and big data. Dr. Ghahramani was a Recipient of the Best Student Paper Award of 2018 IEEE International Conference on Networking, Sensing and Control. He has served as a Reviewer of over 10 journals including IEEE Transactions on Cybernetics, IEEE Transactions on Neural Networks and Learning Systems, and IEEE Transactions on Industrial Informatics

Yan Qiao (M’16) received the B.S. and Ph.D. degrees in industrial engineering and mechanical engineering from Guangdong University of Technology in 2009 and 2015, respectively. From Sept. 2014 to Sept. 2015, he was a Visiting Student with the Department of Electrical and Computer Engineering, New Jersey Institute of Technology, USA. From Jan. 2016 to Dec. 2017, he was a Post-Doctoral Research Associate with the Institute of Systems Engineering, Macau University of Science and Technology. Since Jan. 2018, he is an Assistant Professor with the Institute of Systems Engineering, Macau University of Science and Technology. He has over 60 publications, including one book chapter and over 30 international journal papers. His research interests include discrete event systems, production planning, Petri nets, scheduling, and control. Dr. Qiao was a Recipient of the QSI Best Application Paper Award Finalist of 2011 IEEE International Conference on Automation Science and Engineering, the Best Student Paper Award of 2012 IEEE International Conference on Networking, Sensing and Control, and the Best Conference Paper Award Finalist of 2016 IEEE International Conference on Automation Science and Engineering. He has served as a Reviewer for a number of journals

MengChu Zhou (S’88–M’90–SM’93–F’03) received the B.S. degree in control engineering from Nanjing University of Science and Technology in 1983, the M.S. degree in automatic control from Beijing Institute of Technology in 1986, and the Ph. D. degree in computer and systems engineering from Rensselaer Polytechnic Institute, USA, in 1990. He joined New Jersey Institute of Technology (NJIT), USA, in 1990, and is now a Distinguished Professor of electrical and computer engineering. His research interests include Petri nets, intelligent automation, internet of things (IOT), big data, web services, and intelligent transportation. He has over 800 publications including 12 books, over 500 journal papers (over 400 in IEEE transactions), 12 patents and 29 book-chapters. He is the founding Editor of IEEE Press Book Series on Systems Science and Engineering and Editor-in-Chief of IEEE/CAA Journal of AutomaticaSinica. He is a Recipient of Humboldt Research Award for US Senior Scientists from Alexander von Humboldt Foundation, Franklin V. Taylor Memorial Award and the Norbert Wiener Award from IEEE Systems, Man and Cybernetics Society. He is a Life Member of the Chinese Association for Science and Technology-USA and served as its President in 1999. He is a Fellow of International Federation of Automatic Control (IFAC), American Association for the Advancement of Science (AAAS) and Chinese Association of Automation (CAA)

Adrian O’Hagan is a Lecturer and Researcher in statistics and actuarial science at University College Dublin (UCD), Ireland. He holds the degree in actuarial science and the M.Sc. and Ph.D. degrees in statistics from UCD. He uses cutting edge statistical and data analytics techniques to solve real industrial problems in modelling and pricing risk, working with leading insurers and financial institutions. He currently supervises Ph.D. students in statistical genetics with actuarial applications and statistics and actuarial science, and is currently expanding his research group in the FinTech space. He serves as an Examiner for the Institute and faculty of Actuaries and is a Referee for several leading statistics journals

James Sweeney is a Lecturer and Researcher in statistics at the Royal College of Surgeons Ireland (RCSI), Ireland, with the Ph.D. degree in statistical climatology. His research interests range across the fields of spatial analysis, high performance computing and simulation, and statistical applications in medicine and agriculture. Dr. Sweeney’s core strengths are in the analysis of extremely large datasets, particularly those comprised of multivariate, spatially indexed data with practical applications including the modelling of house price information in Dublin, as well as evaluating the speed and cost of abrupt climate change
Corresponding author: M. C. Zhou is with the Helen and John C. Hartmann Department of Electrical and Computer Engineering, New Jersey Institute of Technology, Newark, NJ 07102 USA (e-mail: zhou@njit.edu)
Received Date: 2020-01-29
Revised Date: 2020-02-22
Accepted Date: 2020-03-06

Available Online: 2020-03-22

Abstract

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 (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.
- Artificial intelligence (AI),
- cyber physical systems,
- feature selection,
- genetic algorithms (GA),
- industrial internet of things (IIOT),
- machine learning,
- neural network (NN),
- smart manufacturing

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References

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Most work in the area of manufacturing data analysis are based on PCA-based approaches. They are not able to recognize nonlinear relationships among features and extract complex pattern. To address this concern, a dynamic feature selection model based on an integrated algorithm including a meta-heuristic method (GA) and an artificial neural network is proposed. The implemented algorithm considers two major conflicting objectives: minimizing the number of features and maximizing the classification performance. The result of the proposed model has been compared with traditional approaches.
The proposed AI-based multi-objective feature selection method together with an efficient classification algorithm can enables decision makers to scrutinize manufacturing processes.

AI-Based Modeling and Data-Driven Evaluation for Smart Manufacturing Processes

doi: 10.1109/JAS.2020.1003114

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