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    Optimization of Electro Discharge Machining Process Parameters With Fuzzy Logic for Stainless Steel 304 (ASTM A240)

    Source: Journal of Manufacturing Science and Engineering:;2018:;volume( 140 ):;issue: 001::page 11013
    Author:
    Ubaid, Alaa M.
    ,
    Dweiri, Fikri T.
    ,
    Aghdeab, Shukry H.
    ,
    Abdullah Al-Juboori, Laith
    DOI: 10.1115/1.4038139
    Publisher: The American Society of Mechanical Engineers (ASME)
    Abstract: Optimization of Electro Discharge Machining Process Parameters With Fuzzy Logic for Stainless Steel 304 (ASTM A240);Electro discharge machining (EDM) process need to be optimized when a new material invented or even if some process variables changed. This process has many variables and it is always difficult to get the optimum set of variables by chance. Therefore, an optimization process need to be conducted considering different combinations of machining parameters as well as other variables even if the process were optimized for a certain set of variables. Optimization of the EDM process for machining stainless steel 304 (SS304) (ASTM A240) was studied in this paper. Signal-to-noise ratio (S/N) was calculated for each performance measures, and multi response performance index (MRPI) was generated using fuzzy logic inference system. Optimal machining parameters for machining SS304 materials were identified, namely current 10, pulse on time 60 μs, and pulse off time 35 μs. Analyses of variances (ANOVA) method was used as well to see which machining parameter has significant effect on the performance measures. The result of ANOVA indicates that pulse off time and current are the most significant machining parameters in affecting the performance measures, with the pulse off time being the most significant parameter.
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      Optimization of Electro Discharge Machining Process Parameters With Fuzzy Logic for Stainless Steel 304 (ASTM A240)

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    https://yetl.yabesh.ir/yetl1/handle/yetl/4252100
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    contributor authorUbaid, Alaa M.
    contributor authorDweiri, Fikri T.
    contributor authorAghdeab, Shukry H.
    contributor authorAbdullah Al-Juboori, Laith
    date accessioned2019-02-28T11:02:59Z
    date available2019-02-28T11:02:59Z
    date copyright11/17/2017 12:00:00 AM
    date issued2018
    identifier issn1087-1357
    identifier othermanu_140_01_011013.pdf
    identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4252100
    description abstractOptimization of Electro Discharge Machining Process Parameters With Fuzzy Logic for Stainless Steel 304 (ASTM A240);Electro discharge machining (EDM) process need to be optimized when a new material invented or even if some process variables changed. This process has many variables and it is always difficult to get the optimum set of variables by chance. Therefore, an optimization process need to be conducted considering different combinations of machining parameters as well as other variables even if the process were optimized for a certain set of variables. Optimization of the EDM process for machining stainless steel 304 (SS304) (ASTM A240) was studied in this paper. Signal-to-noise ratio (S/N) was calculated for each performance measures, and multi response performance index (MRPI) was generated using fuzzy logic inference system. Optimal machining parameters for machining SS304 materials were identified, namely current 10, pulse on time 60 μs, and pulse off time 35 μs. Analyses of variances (ANOVA) method was used as well to see which machining parameter has significant effect on the performance measures. The result of ANOVA indicates that pulse off time and current are the most significant machining parameters in affecting the performance measures, with the pulse off time being the most significant parameter.
    publisherThe American Society of Mechanical Engineers (ASME)
    titleOptimization of Electro Discharge Machining Process Parameters With Fuzzy Logic for Stainless Steel 304 (ASTM A240)
    typeJournal Paper
    journal volume140
    journal issue1
    journal titleJournal of Manufacturing Science and Engineering
    identifier doi10.1115/1.4038139
    journal fristpage11013
    journal lastpage011013-13
    treeJournal of Manufacturing Science and Engineering:;2018:;volume( 140 ):;issue: 001
    contenttypeFulltext
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    DSpace software copyright © 2002-2015  DuraSpace
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