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    Statistical Process Control Based Supervisory Generalized Predictive Control of Thin Film Deposition Processes

    Source: Journal of Manufacturing Science and Engineering:;2006:;volume( 128 ):;issue: 001::page 315
    Author:
    Jionghua Jin
    ,
    Huairui Guo
    ,
    Shiyu Zhou
    DOI: 10.1115/1.2114912
    Publisher: The American Society of Mechanical Engineers (ASME)
    Abstract: This paper presents a supervisory generalized predictive control (GPC) by combining GPC with statistical process control (SPC) for the control of the thin film deposition process. In the supervised GPC, the deposition process is described as an ARMAX model for each production run and GPC is applied to the in situ thickness-sensing data for thickness control. Supervisory strategies, developed from SPC techniques, are used to monitor process changes and estimate the disturbance magnitudes during production. Based on the SPC monitoring results, different supervisory strategies are used to revise the disturbance models and the control law in the GPC to achieve a satisfactory control performance. A case study is provided to demonstrate the developed methodology.
    keyword(s): Thin films , Predictive control , Statistical process control , Errors AND Modeling ,
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      Statistical Process Control Based Supervisory Generalized Predictive Control of Thin Film Deposition Processes

    URI
    http://yetl.yabesh.ir/yetl1/handle/yetl/134232
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    contributor authorJionghua Jin
    contributor authorHuairui Guo
    contributor authorShiyu Zhou
    date accessioned2017-05-09T00:20:50Z
    date available2017-05-09T00:20:50Z
    date copyrightFebruary, 2006
    date issued2006
    identifier issn1087-1357
    identifier otherJMSEFK-27914#315_1.pdf
    identifier urihttp://yetl.yabesh.ir/yetl/handle/yetl/134232
    description abstractThis paper presents a supervisory generalized predictive control (GPC) by combining GPC with statistical process control (SPC) for the control of the thin film deposition process. In the supervised GPC, the deposition process is described as an ARMAX model for each production run and GPC is applied to the in situ thickness-sensing data for thickness control. Supervisory strategies, developed from SPC techniques, are used to monitor process changes and estimate the disturbance magnitudes during production. Based on the SPC monitoring results, different supervisory strategies are used to revise the disturbance models and the control law in the GPC to achieve a satisfactory control performance. A case study is provided to demonstrate the developed methodology.
    publisherThe American Society of Mechanical Engineers (ASME)
    titleStatistical Process Control Based Supervisory Generalized Predictive Control of Thin Film Deposition Processes
    typeJournal Paper
    journal volume128
    journal issue1
    journal titleJournal of Manufacturing Science and Engineering
    identifier doi10.1115/1.2114912
    journal fristpage315
    journal lastpage325
    identifier eissn1528-8935
    keywordsThin films
    keywordsPredictive control
    keywordsStatistical process control
    keywordsErrors AND Modeling
    treeJournal of Manufacturing Science and Engineering:;2006:;volume( 128 ):;issue: 001
    contenttypeFulltext
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    DSpace software copyright © 2002-2015  DuraSpace
    نرم افزار کتابخانه دیجیتال "دی اسپیس" فارسی شده توسط یابش برای کتابخانه های ایرانی | تماس با یابش
    yabeshDSpacePersian