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    Cloud Based Platform for Optimal Machining Parameter Selection Based on Function Blocks and Real Time Monitoring

    Source: Journal of Manufacturing Science and Engineering:;2015:;volume( 137 ):;issue: 004::page 40909
    Author:
    Tapoglou, Nikolaos
    ,
    Mehnen, Jأ¶rn
    ,
    Vlachou, Aikaterini
    ,
    Doukas, Michael
    ,
    Milas, Nikolaos
    ,
    Mourtzis, Dimitris
    DOI: 10.1115/1.4029806
    Publisher: The American Society of Mechanical Engineers (ASME)
    Abstract: The way machining operations have been running has changed over the years. Nowadays, machine utilization and availability monitoring are becoming increasingly important for the smooth operation of modern workshops. Moreover, the nature of jobs undertaken by manufacturing small and medium enterprises (SMEs) has shifted from a mass production to small batch. To address the challenges caused by modern fast changing environments, a new cloudbased approach for monitoring the use of manufacturing equipment, dispatching jobs to the selected computer numerical control (CNC) machines, and creating the optimum machining code is presented. In this approach the manufacturing equipment is monitored using a sensor network and though an information fusion technique it derives and broadcasts the data of available tools and machines through the internet to a cloudbased platform. On the manufacturing equipment event driven function blocks with embedded optimization algorithms are responsible for selecting the optimal cutting parameters and generating the moves required for machining the parts while considering the latest information regarding the available machines and cutting tools. A case study based on scenario from a shop floor that undertakes machining jobs is used to demonstrate the developed methods and tools.
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      Cloud Based Platform for Optimal Machining Parameter Selection Based on Function Blocks and Real Time Monitoring

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    https://yetl.yabesh.ir/yetl1/handle/yetl/158704
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    • Journal of Manufacturing Science and Engineering

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    contributor authorTapoglou, Nikolaos
    contributor authorMehnen, Jأ¶rn
    contributor authorVlachou, Aikaterini
    contributor authorDoukas, Michael
    contributor authorMilas, Nikolaos
    contributor authorMourtzis, Dimitris
    date accessioned2017-05-09T01:20:25Z
    date available2017-05-09T01:20:25Z
    date issued2015
    identifier issn1087-1357
    identifier othermanu_137_04_040909.pdf
    identifier urihttp://yetl.yabesh.ir/yetl/handle/yetl/158704
    description abstractThe way machining operations have been running has changed over the years. Nowadays, machine utilization and availability monitoring are becoming increasingly important for the smooth operation of modern workshops. Moreover, the nature of jobs undertaken by manufacturing small and medium enterprises (SMEs) has shifted from a mass production to small batch. To address the challenges caused by modern fast changing environments, a new cloudbased approach for monitoring the use of manufacturing equipment, dispatching jobs to the selected computer numerical control (CNC) machines, and creating the optimum machining code is presented. In this approach the manufacturing equipment is monitored using a sensor network and though an information fusion technique it derives and broadcasts the data of available tools and machines through the internet to a cloudbased platform. On the manufacturing equipment event driven function blocks with embedded optimization algorithms are responsible for selecting the optimal cutting parameters and generating the moves required for machining the parts while considering the latest information regarding the available machines and cutting tools. A case study based on scenario from a shop floor that undertakes machining jobs is used to demonstrate the developed methods and tools.
    publisherThe American Society of Mechanical Engineers (ASME)
    titleCloud Based Platform for Optimal Machining Parameter Selection Based on Function Blocks and Real Time Monitoring
    typeJournal Paper
    journal volume137
    journal issue4
    journal titleJournal of Manufacturing Science and Engineering
    identifier doi10.1115/1.4029806
    journal fristpage40909
    journal lastpage40909
    identifier eissn1528-8935
    treeJournal of Manufacturing Science and Engineering:;2015:;volume( 137 ):;issue: 004
    contenttypeFulltext
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    DSpace software copyright © 2002-2015  DuraSpace
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