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    Improving Machined Surface Shape Prediction by Integrating Multi-Task Learning With Cutting Force Variation Modeling

    Source: Journal of Manufacturing Science and Engineering:;2017:;volume( 139 ):;issue: 001::page 11014
    Author:
    Shao, Chenhui
    ,
    Ren, Jie
    ,
    Wang, Hui
    ,
    Jin, Jionghua (Judy)
    ,
    Hu, S. Jack
    DOI: 10.1115/1.4034592
    Publisher: The American Society of Mechanical Engineers (ASME)
    Abstract: The shapes of machined surfaces play a critical role affecting powertrain performance, and therefore, it is necessary to characterize the shapes with high resolution. State-of-the-art approaches for surface shape characterization are mostly data-driven by interpolating and extrapolating the spatial data but its precision is limited by the density of measurements. This paper explores the new opportunity of improving surface shape prediction through considering the similarity of multiple similar manufacturing processes. It is a common scenario when the process of interest lacks sufficient data whereas rich data could be available from other similar-but-not-identical processes. It is reasonable to transfer the insights gained from other relevant processes into the surface shape prediction. This paper develops an engineering-guided multitask learning (EG-MTL) surface model by fusing surface cutting physics in engineering processes and the spatial data from a number of similar-but-not-identical processes. An iterative multitask Gaussian process learning algorithm is developed to learn the model parameters. Compared with the conventional multitask learning, the proposed method has the advantages in incorporating the insights on cutting force variation during machining and is potentially able to improve the prediction performance given limited measurement data. The methodology is demonstrated based on the data from real-world machining processes in an engine plant.
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      Improving Machined Surface Shape Prediction by Integrating Multi-Task Learning With Cutting Force Variation Modeling

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    http://yetl.yabesh.ir/yetl1/handle/yetl/4234662
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    contributor authorShao, Chenhui
    contributor authorRen, Jie
    contributor authorWang, Hui
    contributor authorJin, Jionghua (Judy)
    contributor authorHu, S. Jack
    date accessioned2017-11-25T07:17:35Z
    date available2017-11-25T07:17:35Z
    date copyright2016/29/9
    date issued2017
    identifier issn1087-1357
    identifier othermanu_139_01_011014.pdf
    identifier urihttp://138.201.223.254:8080/yetl1/handle/yetl/4234662
    description abstractThe shapes of machined surfaces play a critical role affecting powertrain performance, and therefore, it is necessary to characterize the shapes with high resolution. State-of-the-art approaches for surface shape characterization are mostly data-driven by interpolating and extrapolating the spatial data but its precision is limited by the density of measurements. This paper explores the new opportunity of improving surface shape prediction through considering the similarity of multiple similar manufacturing processes. It is a common scenario when the process of interest lacks sufficient data whereas rich data could be available from other similar-but-not-identical processes. It is reasonable to transfer the insights gained from other relevant processes into the surface shape prediction. This paper develops an engineering-guided multitask learning (EG-MTL) surface model by fusing surface cutting physics in engineering processes and the spatial data from a number of similar-but-not-identical processes. An iterative multitask Gaussian process learning algorithm is developed to learn the model parameters. Compared with the conventional multitask learning, the proposed method has the advantages in incorporating the insights on cutting force variation during machining and is potentially able to improve the prediction performance given limited measurement data. The methodology is demonstrated based on the data from real-world machining processes in an engine plant.
    publisherThe American Society of Mechanical Engineers (ASME)
    titleImproving Machined Surface Shape Prediction by Integrating Multi-Task Learning With Cutting Force Variation Modeling
    typeJournal Paper
    journal volume139
    journal issue1
    journal titleJournal of Manufacturing Science and Engineering
    identifier doi10.1115/1.4034592
    journal fristpage11014
    journal lastpage011014-11
    treeJournal of Manufacturing Science and Engineering:;2017:;volume( 139 ):;issue: 001
    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