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    A Bayesian Model of Machining Economics for Optimization by Adaptive Control

    Source: Journal of Manufacturing Science and Engineering:;1970:;volume( 092 ):;issue: 003::page 628
    Author:
    Donald S. Ermer
    DOI: 10.1115/1.3427825
    Publisher: The American Society of Mechanical Engineers (ASME)
    Abstract: A learning model of tool wear based on Bayesian statistical methods provides a means for regulating the optimum cutting conditions as periodic sampling data on flank wear become available during production under adaptive control. The sampling process is used to estimate the current parameters of the wear process, and by incorporating this updated information into the machining economics model, an optimal a posteriori program of cutting conditions can be determined to best match the current conditions of the tool, workpiece, and machine. The application of the Bayesian learning model is illustrated for a basic turning operation with minimum cost as the optimizing criterion.
    keyword(s): Machining , Adaptive control , Economics , Optimization , Wear , Cutting , Sampling (Acoustical engineering) , Machinery AND Turning ,
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      A Bayesian Model of Machining Economics for Optimization by Adaptive Control

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    http://yetl.yabesh.ir/yetl1/handle/yetl/145057
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    contributor authorDonald S. Ermer
    date accessioned2017-05-09T00:41:43Z
    date available2017-05-09T00:41:43Z
    date copyrightAugust, 1970
    date issued1970
    identifier issn1087-1357
    identifier otherJMSEFK-27554#628_1.pdf
    identifier urihttp://yetl.yabesh.ir/yetl/handle/yetl/145057
    description abstractA learning model of tool wear based on Bayesian statistical methods provides a means for regulating the optimum cutting conditions as periodic sampling data on flank wear become available during production under adaptive control. The sampling process is used to estimate the current parameters of the wear process, and by incorporating this updated information into the machining economics model, an optimal a posteriori program of cutting conditions can be determined to best match the current conditions of the tool, workpiece, and machine. The application of the Bayesian learning model is illustrated for a basic turning operation with minimum cost as the optimizing criterion.
    publisherThe American Society of Mechanical Engineers (ASME)
    titleA Bayesian Model of Machining Economics for Optimization by Adaptive Control
    typeJournal Paper
    journal volume92
    journal issue3
    journal titleJournal of Manufacturing Science and Engineering
    identifier doi10.1115/1.3427825
    journal fristpage628
    journal lastpage632
    identifier eissn1528-8935
    keywordsMachining
    keywordsAdaptive control
    keywordsEconomics
    keywordsOptimization
    keywordsWear
    keywordsCutting
    keywordsSampling (Acoustical engineering)
    keywordsMachinery AND Turning
    treeJournal of Manufacturing Science and Engineering:;1970:;volume( 092 ):;issue: 003
    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
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