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    Quality and Inspection of Machining Operations: Tool Condition Monitoring

    Source: Journal of Manufacturing Science and Engineering:;2010:;volume( 132 ):;issue: 004::page 41015
    Author:
    John T. Roth
    ,
    Dragan Djurdjanovic
    ,
    Xiaoping Yang
    ,
    Laine Mears
    ,
    Thomas Kurfess
    DOI: 10.1115/1.4002022
    Publisher: The American Society of Mechanical Engineers (ASME)
    Abstract: Tool condition monitoring (TCM) is an important aspect of condition based maintenance (CBM) in all manufacturing processes. Recent work on TCM has generated significant successes for a variety of cutting operations. In particular, lower cost and on-board sensors in conjunction with enhanced signal processing capabilities and improved networking has permitted significant enhancements to TCM capabilities. This paper presents an overview of TCM for drilling, turning, milling, and grinding. The focus of this paper is on the hardware and algorithms that have demonstrated success in TCM for these processes. While a variety of initial successes are reported, significantly more research is possible to extend the capabilities of TCM for the reported cutting processes as well as for many other manufacturing processes. Furthermore, no single unifying approach has been identified for TCM. Such an approach will enable the rapid expansion of TCM into other processes and a tighter integration of TCM into CBM for a wide variety of manufacturing processes and production systems.
    keyword(s): Force , Wear , Sensors , Hardware , Signal processing , Artificial neural networks , Condition monitoring , Cutting , Feature extraction , Signals , Grinding , Milling , Drilling , Machining AND Spindles (Textile machinery) ,
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      Quality and Inspection of Machining Operations: Tool Condition Monitoring

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    contributor authorJohn T. Roth
    contributor authorDragan Djurdjanovic
    contributor authorXiaoping Yang
    contributor authorLaine Mears
    contributor authorThomas Kurfess
    date accessioned2017-05-09T00:39:18Z
    date available2017-05-09T00:39:18Z
    date copyrightAugust, 2010
    date issued2010
    identifier issn1087-1357
    identifier otherJMSEFK-28393#041015_1.pdf
    identifier urihttp://yetl.yabesh.ir/yetl/handle/yetl/144032
    description abstractTool condition monitoring (TCM) is an important aspect of condition based maintenance (CBM) in all manufacturing processes. Recent work on TCM has generated significant successes for a variety of cutting operations. In particular, lower cost and on-board sensors in conjunction with enhanced signal processing capabilities and improved networking has permitted significant enhancements to TCM capabilities. This paper presents an overview of TCM for drilling, turning, milling, and grinding. The focus of this paper is on the hardware and algorithms that have demonstrated success in TCM for these processes. While a variety of initial successes are reported, significantly more research is possible to extend the capabilities of TCM for the reported cutting processes as well as for many other manufacturing processes. Furthermore, no single unifying approach has been identified for TCM. Such an approach will enable the rapid expansion of TCM into other processes and a tighter integration of TCM into CBM for a wide variety of manufacturing processes and production systems.
    publisherThe American Society of Mechanical Engineers (ASME)
    titleQuality and Inspection of Machining Operations: Tool Condition Monitoring
    typeJournal Paper
    journal volume132
    journal issue4
    journal titleJournal of Manufacturing Science and Engineering
    identifier doi10.1115/1.4002022
    journal fristpage41015
    identifier eissn1528-8935
    keywordsForce
    keywordsWear
    keywordsSensors
    keywordsHardware
    keywordsSignal processing
    keywordsArtificial neural networks
    keywordsCondition monitoring
    keywordsCutting
    keywordsFeature extraction
    keywordsSignals
    keywordsGrinding
    keywordsMilling
    keywordsDrilling
    keywordsMachining AND Spindles (Textile machinery)
    treeJournal of Manufacturing Science and Engineering:;2010:;volume( 132 ):;issue: 004
    contenttypeFulltext
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