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Volume 7 Issue 4
Jun.  2020

IEEE/CAA Journal of Automatica Sinica

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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
Citation: 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

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)
More Information
  • 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.

     

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    Highlights

    • 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.

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