A journal of IEEE and CAA , publishes high-quality papers in English on original theoretical/experimental research and development in all areas of automation
Volume 7 Issue 4
Jun.  2020

IEEE/CAA Journal of Automatica Sinica

  • JCR Impact Factor: 6.171, Top 11% (SCI Q1)
    CiteScore: 11.2, Top 5% (Q1)
    Google Scholar h5-index: 51, TOP 8
Turn off MathJax
Article Contents
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.

     

  • loading
  • [1]
    S. Jeschke, C. Brecher, T. Meisen, D. Ozdemir, and T. Eschert, “Industrial internet of things and cyber manufacturing systems,” Industrial Internet of Things,Springer Series in Wireless Technology,Springer,Cham, pp. 3–19, 2017.
    [2]
    T. Lojka, M. Miskuf M., and I. Zolotova, “Industrial IOT gateway with machine learning for smart manufacturing,” IFIP Advances in Information and Communication Technology, pp. 759–766, 2017.
    [3]
    J. Tavcar and I. Horvath, “A Review of the principles of designing smart cyber-physical systems for run-time adaptation: learned lessons and open issues,” IEEE Trans. Systems,Man,and Cybernetics:Systems, vol. 49, pp. 145–158, 2019. doi: 10.1109/TSMC.2018.2814539
    [4]
    B. Chen, D. W. C. Ho, W. Zhang, and L. Yu, “Distributed dimensionality reduction fusion estimation for cyber-physical systems under DoS attacks,” IEEE Trans. Systems,Man,and Cybernetics:Systems, vol. 49, pp. 455–468, 2019. doi: 10.1109/TSMC.2017.2697450
    [5]
    P. Palensky, E. Widl, and A. Elsheikh, “Simulating cyber-physical energy systems: challenges, tools and methods,” IEEE Trans. Systems,Man,and Cybernetics:Systems, vol. 44, pp. 318–326, 2014. doi: 10.1109/TSMCC.2013.2265739
    [6]
    Y. Liu, Y. Peng, B. Wang, S. Yao, and Z. Liu, “Review on cyber-physical systems,” IEEE/CAA J. Autom. Sinica, vol. 4, pp. 27–40, 2017. doi: 10.1109/JAS.2017.7510349
    [7]
    G. Fortino, F. Messina, D. Rosaci, G. M. Sarne, and C. Savaglio, “A trust-based team formation framework for mobile intelligence in smart factories,” IEEE Trans. Industrial Informatics, 2020. doi: 10.1109/TII.2020.2963910
    [8]
    P. Zhang, M. Zhou, and G. Fortino, “Security and trust issues in Fog computing: A survey,” Future Generation Computer Systems, vol. 88, pp. 16–27, Nov. 2018.
    [9]
    M. Ghahramani, M. C. Zhou, and C. T. Hon, “Extracting significant mobile phone interaction patterns based on community structures,” IEEE Trans. Intelligent Transportation Systems, vol. 20, pp. 1031–1041, 2019. doi: 10.1109/TITS.2018.2836800
    [10]
    M. Ghahramani, M. C. Zhou, and C. T. Hon, “Mobile phone data analysis: a spatial exploration toward hotspot detection,” IEEE Trans. Automation Science and Engineering, vol. 16, pp. 351–362, 2019. doi: 10.1109/TASE.2018.2795241
    [11]
    M. H. Ghahramani, M. C. Zhou, and C. T. Hon, “Toward cloud computing QoS architecture: analysis of cloud systems and cloud services,” IEEE/CAA J. Autom. Sinica, vol. 4, pp. 6–18, 2017. doi: 10.1109/JAS.2017.7510313
    [12]
    D. P. Bertsekas, “Feature-based aggregation and deep reinforcement learning: a survey and some new implementations,” IEEE/CAA J. Autom. Sinica, vol. 6, no. 1, pp. 1–31, Jan. 2019. doi: 10.1109/JAS.2018.7511249
    [13]
    Z. Gao, C. Cecati, and S. X. Ding, “A survey of fault diagnosis and fault-tolerant techniques, part II: fault diagnosis with knowledge-based and hybrid/active approaches,” IEEE Trans. Industrial Electronics, vol. 62, pp. 3768–3774, 2015.
    [14]
    S. P. Hoseini Alinodehi, S. Moshfe, M. Saber Zaeimian, A. Khoei, and K. Hadidi, “High-speed general purpose genetic algorithm processor,” IEEE Trans. Cybernetics, vol. 46, pp. 1551–1565, 2016. doi: 10.1109/TCYB.2015.2451595
    [15]
    J. Wan, S. Tang, D. Li, S. Wang, C. Liu, H. Abbas, and A. V. Vasilakos, “A manufacturing big data solution for active preventive maintenance,” IEEE Trans. Industrial Informatics, vol. 13, pp. 2039–2047, 2017. doi: 10.1109/TII.2017.2670505
    [16]
    S. M. Meerkov and M. T. Ravichandran, “Combating curse of dimensionality in resilient monitoring systems: conditions for lossless decomposition,” IEEE Trans. Cybernetics, vol. 47, pp. 1263–1272, 2017. doi: 10.1109/TCYB.2016.2543701
    [17]
    Q. P. He and J. Wang, “Principal component based k-nearest-neighbor rule for semiconductor process fault detection,” in Proc. American Control Conf., Seattle, USA, 2008, DOI: 10.1109/ACC.2008.4586721.
    [18]
    G. A. Cherry and S. J. Qin, “Principal component based k-nearestneighbor rule for semiconductor process fault detection,” IEEE Trans. Semiconductor Manufacturing, vol. 19, pp. 159–172, 2006. doi: 10.1109/TSM.2006.873524
    [19]
    S. He, G. Wang, M. Zhang, and D. Cook, “Multivariate process monitoring and fault identification using multiple decision tree classifiers,” Int. J. Production Research, pp. 3355–3371, 2013.
    [20]
    Q. He and J. Wang, “Fault detection using the k-nearest neighbor rule for semiconductor manufacturing processes,” IEEE Trans. Semiconductor Manufacturing, vol. 20, no. 4, pp. 345–354, 2007. doi: 10.1109/TSM.2007.907607
    [21]
    G. Verdier and A. Ferreira, “Adaptive mahalanobis distance and k-nearest neighbor rule for fault detection in semiconductor manufacturing,” IEEE Trans. Semiconductor Manufacturing, vol. 24, no. 1, pp. 59–68, 2011. doi: 10.1109/TSM.2010.2065531
    [22]
    R. Baly and H. Hajj, “Wafer classification using support vector machines,” IEEE Trans. Semiconductor Manufacturing, vol. 25, no. 3, pp. 373–383, 2012. doi: 10.1109/TSM.2012.2196058
    [23]
    J. Kwak, T. Lee, and C. O. Kim, “An Incremental clustering-based fault detection algorithm for class-imbalanced process data,” IEEE Trans. Semiconductor Manufacturing, vol. 28, no. 3, pp. 318–328, 2015. doi: 10.1109/TSM.2015.2445380
    [24]
    Y. Zheng, Q. Liu, E. Chen, Y. Ge, and J. Zhao, “Time series classification using multi-channels deep convolutional neural networks,” in Proc. WAIM, Macau, pp. 298–310, 2014.
    [25]
    K. Lee, S. Cheon, and C. Kim, “A convolutional neural network for fault classification and diagnosis in semiconductor manufacturing processes,” IEEE Trans. Semiconductor Manufacturing, vol. 30, pp. 135–142, May 2017. doi: 10.1109/TSM.2017.2676245
    [26]
    J. Zhang, H. Chen, S. Chen, and X. Hong, “An improved mixture of probabilistic PCA for nonlinear data-driven process monitoring,” IEEE Trans. Cybernetics, vol. 49, 2019.
    [27]
    B. Xue, M. Zhang, W. N. Browne, and Xin Yao, “A survey on evolutionary computation approaches to feature selection,” IEEE Trans. Evolutionary Computation, vol. 20, pp. 606–626, 2016. doi: 10.1109/TEVC.2015.2504420
    [28]
    J. Derrac, S. Garcia, and F. Herrera, “A first study on the use of coevolutionary algorithms for instance and feature selection,” in Proc. Hybrid Artificial Intelligence Systems, Berlin, Germany, 2009.
    [29]
    M. Zamalloa, G. Bordel, L. J. Rodriguez, and M. Penagarikano, “Feature selection based on genetic algorithms for speaker recognition,” in Proc. IEEE Odyssey Speaker Lang. Recognit. Workshop, USA, 2006, pp. 1–8.
    [30]
    L. Dong, S. Chai, B. Zhang, S. K. Nguang, and A. Savvaris, “Stability of a class of multiagent tracking systems with unstable subsystems,” IEEE Trans. Cybernetics, vol. 47, pp. 2193–2202, 2017. doi: 10.1109/TCYB.2016.2581830
    [31]
    S. Wang and X. Yao, “Multiclass imbalance problems: analysis and potential solutions,” IEEE Trans. Systems,Man,and Cybernetics,Part B(Cybernetics), vol. 42, pp. 1119–1130, 2012. doi: 10.1109/TSMCB.2012.2187280
    [32]
    H. Liu, M. Zhou and Q. Liu, “An embedded feature selection method for imbalanced data classification,” IEEE/CAA J. Autom. Sinica, vol. 6, no. 3, pp. 703–715, May 2019. doi: 10.1109/JAS.2019.1911447
    [33]
    Q. Kang, X. Chen, S. Li, and M. C. Zhou, “A noise-filtered undersampling scheme for imbalanced classification,” IEEE Trans. Cybernetics, vol. 47, no. 12, pp. 4263–4274, Dec. 2018.
    [34]
    C. Bunkhumpornpat, K. Sinapiromsaran, and C. Lursinsap, “DBSMOTE: density based synthetic minority over-sampling technique,” Applied Intelligence, vol. 36, pp. 1–21, 2011.
    [35]
    X. Zhang, Y. Zhuang, W. Wang, and W. Pedrycz, “Transfer boosting with synthetic instances for class imbalanced object recognition,” IEEE Trans. Cybernetics, vol. 48, 2018.
    [36]
    A. Gupta, Y. Ong, L. Feng, and K. Tan, “Multiobjective multifactorial optimization in evolutionary multitasking,” IEEE Trans. Cybernetics, vol. 49, pp. 1652–1665, 2017.
    [37]
    C. Hou, F. Nie, X. Li, D. Yi, and Y. Wu, “Joint embedding learning and sparse regression: a framework for unsupervised feature selection,” IEEE Trans. Cybernetics, vol. 44, pp. 793–804, 2013.
    [38]
    C. Hou, F. Nie, X. Li, D. Yi, and Y. Wu, “A survey on feature selection methods,” Computers&Electrical Engineering, vol. 40, no. 1, pp. 16–28, 2014.
    [39]
    G. Tian, Y. Ren, Y. Feng, M. Zhou, H. Zhang, and J. Tan, “Modeling and planning for dual-objective selective disassembly using and/or graph and discrete artificial bee colony,” IEEE Trans. Industrial Informatics, vol. 15, no. 4, pp. 2456–2468, Apr. 2019.
    [40]
    X. Zuo, B. Li, X. Huang, M. Zhou, C. Cheng, X. Zhao, and Z. Liu, “Optimizing hospital emergency department layout via multiobjective tabu search,” IEEE Trans. Automation Science and Engineering, vol. 16, no. 3, pp. 1137–1147, Jul. 2019.
    [41]
    Q. Kang, X. Song, M. Zhou, and L. Li, “A collaborative resource allocation strategy for decomposition-based multiobjective evolutionary algorithms,” IEEE Trans. Systems,Man,and Cybernetics:Systems, vol. 49, no. 12, pp. 2416–2423, Dec. 2019.
    [42]
    Z. Lv, L. Wang, Z. Han, J. Zhao, and W. Wang, “Surrogate-assisted particle swarm optimization algorithm with Pareto active learning for expensive multi-objective optimization,” IEEE/CAA J. Autom. Sinica, vol. 6, no. 3, pp. 838–849, May 2019.
    [43]
    J. Li, H. Sang, Q. Pan, P. Duan, and K. Gao, “Solving multi-area environmental/ economic dispatch by Pareto-based chemical-reaction optimization algorithm,” IEEE/CAA J. Autom. Sinica, vol. 6, no. 5, pp. 1240–1250, Sept. 2019.
    [44]
    J. Sun, S. Gao, H. Dai, J. Cheng, M. Zhou, and J. Wang, “Bi-objective elite differential evolution algorithm for multivalued logic networks,” IEEE Trans. Cybernetics, vol. 50, no. 1, pp. 233–246, Jan. 2020.
    [45]
    L. Huang, M. Zhou, and K. Hao, “Non-dominated immune-endocrine short feedback algorithm for multi-robot maritime patrolling,” IEEE Trans. Intelligent Transportation Systems, vol. 21, no. 1, pp. 362–373, Jan. 2020.
    [46]
    Q. Wu, M. Zhou, Q. Zhu, Y. Xia, and J. Wen, “MOELS: Multiobjective evolutionary list scheduling for cloud workflows,” IEEE Trans. Automation Science and Engineering, vol. 17, no. 1, pp. 166–176, Jan. 2020.
    [47]
    H. Jia, H. Miao, G. Tian, M. Zhou, Y. Feng, Z. Li, and J. Li, “Multiobjective bike repositioning in bike-sharing systems via a modified artificial bee colony algorithm,” IEEE Trans. Automation Science and Engineering, vol. 17, no. 2, pp. 621–632, Apr. 2020.
    [48]
    S. Gao, M. Zhou, Y. Wang, J. Cheng, H. Yachi, and J. Wang, “Dendritic neuron model with effective learning algorithms for classification, approximation and prediction,” IEEE Trans. Neural Networks and Learning Systems, vol. 30, no. 2, pp. 601–614, Feb. 2019.
    [49]
    H. Yuan, J. Bi, and M. Zhou, “Spatial task scheduling for cost minimization in distributed green cloud data centers,” IEEE Trans. Automation Science and Engineering, vol. 16, no. 2, pp. 729–740, Apr. 2019.
    [50]
    H. Yuan, J. Bi, and M. Zhou, “Spatiotemporal task scheduling for heterogeneous delay-tolerant applications in distributed green data centers,” IEEE Trans. Automation Science and Engineering, vol. 16, no. 4, pp. 1686–1697, Oct. 2019.
    [51]
    W. Hu, Y. Huang, F. Zhang, and R. Li, “Noise-tolerant paradigm for training face recognition CNNs,” in Proc. IEEE Conf. Computer Vision and Pattern Recognition, pp. 11887–11896, 2019.

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索

    Figures(7)  / Tables(3)

    Article Metrics

    Article views (1297) PDF downloads(59) Cited by()

    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.

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return