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    Surface Roughness Characterization and Inversion of Ultrasonic Grinding Parameters Based on Support Vector Machine

    Source: Journal of Tribology:;2022:;volume( 144 ):;issue: 009::page 94501-1
    Author:
    Duo
    ,
    Yang;Jinyuan
    ,
    Tang;Fujia
    ,
    Xia;Wei
    ,
    Zhou
    DOI: 10.1115/1.4054234
    Publisher: The American Society of Mechanical Engineers (ASME)
    Abstract: With surface roughness restricted by grinding parameters, the characterization of roughness parameters and the inversion of grinding parameters are of great significance for improving surface performance and realizing active surface machining. This research proposes a combination of statistical theory and data-driven analysis to solve the above problems. Pearson correlation analysis and multivariate variance analysis indicate the correlation characterization parameter set (CPS) consists of Sa, Vmp, Vvv, and Sz and that there are differences in the influence of grinding parameters on the parameters in CPS. Adjustment of support vector machine (SVM) core parameters makes it possible to construct expansion parameter set (EPS) optimal inversion models. By designing pseudo-surface random roughness parameters and grinding experiments, the reliability of inversion models is verified. The results show: (1) The better generalization of inversion model indicates skewness Ssk and kurtosis Sku in EPS have important implications for the optimal inversion model and surface characterization and (2) The data-driven model based on support vector machine provides machining guidance for obtaining the expected ultrasonic grinding surface.
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      Surface Roughness Characterization and Inversion of Ultrasonic Grinding Parameters Based on Support Vector Machine

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    http://yetl.yabesh.ir/yetl1/handle/yetl/4287482
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    contributor authorDuo
    contributor authorYang;Jinyuan
    contributor authorTang;Fujia
    contributor authorXia;Wei
    contributor authorZhou
    date accessioned2022-08-18T13:07:49Z
    date available2022-08-18T13:07:49Z
    date copyright5/3/2022 12:00:00 AM
    date issued2022
    identifier issn0742-4787
    identifier othertrib_144_9_094501.pdf
    identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4287482
    description abstractWith surface roughness restricted by grinding parameters, the characterization of roughness parameters and the inversion of grinding parameters are of great significance for improving surface performance and realizing active surface machining. This research proposes a combination of statistical theory and data-driven analysis to solve the above problems. Pearson correlation analysis and multivariate variance analysis indicate the correlation characterization parameter set (CPS) consists of Sa, Vmp, Vvv, and Sz and that there are differences in the influence of grinding parameters on the parameters in CPS. Adjustment of support vector machine (SVM) core parameters makes it possible to construct expansion parameter set (EPS) optimal inversion models. By designing pseudo-surface random roughness parameters and grinding experiments, the reliability of inversion models is verified. The results show: (1) The better generalization of inversion model indicates skewness Ssk and kurtosis Sku in EPS have important implications for the optimal inversion model and surface characterization and (2) The data-driven model based on support vector machine provides machining guidance for obtaining the expected ultrasonic grinding surface.
    publisherThe American Society of Mechanical Engineers (ASME)
    titleSurface Roughness Characterization and Inversion of Ultrasonic Grinding Parameters Based on Support Vector Machine
    typeJournal Paper
    journal volume144
    journal issue9
    journal titleJournal of Tribology
    identifier doi10.1115/1.4054234
    journal fristpage94501-1
    journal lastpage94501-6
    page6
    treeJournal of Tribology:;2022:;volume( 144 ):;issue: 009
    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
    yabeshDSpacePersian