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    Analytical Elastic–Plastic Cutting Model for Predicting Grain Depth-of-Cut in Ultrafine Grinding of Silicon Wafer

    Source: Journal of Manufacturing Science and Engineering:;2018:;volume( 140 ):;issue: 012::page 121001
    Author:
    Lin, Bin
    ,
    Zhou, Ping
    ,
    Wang, Ziguang
    ,
    Yan, Ying
    ,
    Kang, Renke
    ,
    Guo, Dongming
    DOI: 10.1115/1.4041245
    Publisher: The American Society of Mechanical Engineers (ASME)
    Abstract: Grain depth-of-cut, which is the predominant factor determining the surface morphology, grinding force, and subsurface damage, has a significant impact on the surface quality of the finished part made of hard and brittle materials. When the existing analytical models are used to predict the gain depth-of-cut in ultra-precision grinding process of silicon wafer, the results obtained become unreasonable due to an extremely shallow grain depth-of-cut, which is inconsistent with the theory of the contact mechanics. In this study, an improved model for analyzing the grain depth-of-cut in ultra-fine rotational grinding is proposed, in which the minimum grain depth-of-cut for chip formation, the equivalent grain cutting tip radius, elastic recovery deformation in cutting process, and the actual number of effective grains are considered in the prediction of the ultrafine rotational grinding of brittle materials. The improved model is validated experimentally and shows higher accuracy than the existing model. Furthermore, the sensitivity of the grain depth-of-cut to three introduced factors is analyzed, presenting the necessity of the consideration of these factors during the prediction of grain depth-of-cut in ultrafine grinding.
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      Analytical Elastic–Plastic Cutting Model for Predicting Grain Depth-of-Cut in Ultrafine Grinding of Silicon Wafer

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    http://yetl.yabesh.ir/yetl1/handle/yetl/4252018
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    contributor authorLin, Bin
    contributor authorZhou, Ping
    contributor authorWang, Ziguang
    contributor authorYan, Ying
    contributor authorKang, Renke
    contributor authorGuo, Dongming
    date accessioned2019-02-28T11:02:32Z
    date available2019-02-28T11:02:32Z
    date copyright9/17/2018 12:00:00 AM
    date issued2018
    identifier issn1087-1357
    identifier othermanu_140_12_121001.pdf
    identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4252018
    description abstractGrain depth-of-cut, which is the predominant factor determining the surface morphology, grinding force, and subsurface damage, has a significant impact on the surface quality of the finished part made of hard and brittle materials. When the existing analytical models are used to predict the gain depth-of-cut in ultra-precision grinding process of silicon wafer, the results obtained become unreasonable due to an extremely shallow grain depth-of-cut, which is inconsistent with the theory of the contact mechanics. In this study, an improved model for analyzing the grain depth-of-cut in ultra-fine rotational grinding is proposed, in which the minimum grain depth-of-cut for chip formation, the equivalent grain cutting tip radius, elastic recovery deformation in cutting process, and the actual number of effective grains are considered in the prediction of the ultrafine rotational grinding of brittle materials. The improved model is validated experimentally and shows higher accuracy than the existing model. Furthermore, the sensitivity of the grain depth-of-cut to three introduced factors is analyzed, presenting the necessity of the consideration of these factors during the prediction of grain depth-of-cut in ultrafine grinding.
    publisherThe American Society of Mechanical Engineers (ASME)
    titleAnalytical Elastic–Plastic Cutting Model for Predicting Grain Depth-of-Cut in Ultrafine Grinding of Silicon Wafer
    typeJournal Paper
    journal volume140
    journal issue12
    journal titleJournal of Manufacturing Science and Engineering
    identifier doi10.1115/1.4041245
    journal fristpage121001
    journal lastpage121001-7
    treeJournal of Manufacturing Science and Engineering:;2018:;volume( 140 ):;issue: 012
    contenttypeFulltext
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