YaBeSH Engineering and Technology Library

    • Journals
    • PaperQuest
    • YSE Standards
    • YaBeSH
    • Login
    View Item 
    •   YE&T Library
    • ASME
    • Journal of Manufacturing Science and Engineering
    • View Item
    •   YE&T Library
    • ASME
    • Journal of Manufacturing Science and Engineering
    • View Item
    • All Fields
    • Source Title
    • Year
    • Publisher
    • Title
    • Subject
    • Author
    • DOI
    • ISBN
    Advanced Search
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    Archive

    Intelligent Real-Time Predictive Diagnostics for Cutting Tools and Supervisory Control of Machining Operations

    Source: Journal of Manufacturing Science and Engineering:;1993:;volume( 115 ):;issue: 003::page 268
    Author:
    K. Ramamurthi
    ,
    C. L. Hough
    DOI: 10.1115/1.2901660
    Publisher: The American Society of Mechanical Engineers (ASME)
    Abstract: Machining economics may be improved by automating the replacement of cutting tools. In-process diagnosis of the cutting tool using multiple sensors is essential for such automation. In this study, an intelligent real-time diagnostic system is developed and applied towards that objective. A generalized Machining Influence Diagram (MID) is formulated for modeling different modes of failure in conventional metal cutting processes. A faster algorithm for this model is developed to solve the diagnostic problem in real-time applications. A formal methodology is outlined to tune the knowledge base during training with a reduction in training time. Finally, the system is implemented on a drilling machine and evaluated on-line. The on-line response is well within the desired response time of actual production lines. The instance and the accuracy of diagnosis are quite promising. In cases where drill wear is not diagnosed in a timely manner, the system predicts wear induced failure and vice versa. By diagnosing at least one of the two failure modes, the system is able to prevent any abrupt failure of the drill during machining.
    keyword(s): Machining , Cutting tools , Failure , Patient diagnosis , Drills (Tools) , Wear , Machinery , Assembly lines , Sensors , Algorithms , Economics , Modeling , Metal cutting AND Drilling ,
    • Download: (1.283Mb)
    • Show Full MetaData Hide Full MetaData
    • Get RIS
    • Item Order
    • Go To Publisher
    • Price: 5000 Rial
    • Statistics

      Intelligent Real-Time Predictive Diagnostics for Cutting Tools and Supervisory Control of Machining Operations

    URI
    http://yetl.yabesh.ir/yetl1/handle/yetl/112228
    Collections
    • Journal of Manufacturing Science and Engineering

    Show full item record

    contributor authorK. Ramamurthi
    contributor authorC. L. Hough
    date accessioned2017-05-08T23:41:50Z
    date available2017-05-08T23:41:50Z
    date copyrightAugust, 1993
    date issued1993
    identifier issn1087-1357
    identifier otherJMSEFK-27765#268_1.pdf
    identifier urihttp://yetl.yabesh.ir/yetl/handle/yetl/112228
    description abstractMachining economics may be improved by automating the replacement of cutting tools. In-process diagnosis of the cutting tool using multiple sensors is essential for such automation. In this study, an intelligent real-time diagnostic system is developed and applied towards that objective. A generalized Machining Influence Diagram (MID) is formulated for modeling different modes of failure in conventional metal cutting processes. A faster algorithm for this model is developed to solve the diagnostic problem in real-time applications. A formal methodology is outlined to tune the knowledge base during training with a reduction in training time. Finally, the system is implemented on a drilling machine and evaluated on-line. The on-line response is well within the desired response time of actual production lines. The instance and the accuracy of diagnosis are quite promising. In cases where drill wear is not diagnosed in a timely manner, the system predicts wear induced failure and vice versa. By diagnosing at least one of the two failure modes, the system is able to prevent any abrupt failure of the drill during machining.
    publisherThe American Society of Mechanical Engineers (ASME)
    titleIntelligent Real-Time Predictive Diagnostics for Cutting Tools and Supervisory Control of Machining Operations
    typeJournal Paper
    journal volume115
    journal issue3
    journal titleJournal of Manufacturing Science and Engineering
    identifier doi10.1115/1.2901660
    journal fristpage268
    journal lastpage277
    identifier eissn1528-8935
    keywordsMachining
    keywordsCutting tools
    keywordsFailure
    keywordsPatient diagnosis
    keywordsDrills (Tools)
    keywordsWear
    keywordsMachinery
    keywordsAssembly lines
    keywordsSensors
    keywordsAlgorithms
    keywordsEconomics
    keywordsModeling
    keywordsMetal cutting AND Drilling
    treeJournal of Manufacturing Science and Engineering:;1993:;volume( 115 ):;issue: 003
    contenttypeFulltext
    DSpace software copyright © 2002-2015  DuraSpace
    نرم افزار کتابخانه دیجیتال "دی اسپیس" فارسی شده توسط یابش برای کتابخانه های ایرانی | تماس با یابش
    yabeshDSpacePersian
     
    DSpace software copyright © 2002-2015  DuraSpace
    نرم افزار کتابخانه دیجیتال "دی اسپیس" فارسی شده توسط یابش برای کتابخانه های ایرانی | تماس با یابش
    yabeshDSpacePersian