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    Hybrid Modeling of Position-Dependent Dynamics of Thin-Walled Parts Using Shell Elements for Milling Simulation

    Source: Journal of Manufacturing Science and Engineering:;2022:;volume( 144 ):;issue: 008::page 81014-1
    Author:
    Karimi, Behnam
    ,
    Altintas, Yusuf
    DOI: 10.1115/1.4053596
    Publisher: The American Society of Mechanical Engineers (ASME)
    Abstract: This article presents a hybrid model to update the position-dependent structural dynamic parameters of thin-walled workpieces as the metal is removed during machining. The initial workpiece is modeled by shell elements, and its full stiffness and mass matrices are used to solve the eigenvalues and mode shapes to predict the frequency response function (FRF) at a fixed location. The model is calibrated using the experimentally measured FRF, which reduces the errors contributed by the uncertainties in the material properties and damping values. The optimized finite element (FE) model is then perturbed at discrete cutting locations to obtain the updated natural frequencies and mode shapes of the part without solving the computationally prohibitive eigenvalue problem. The accuracy of the model is further improved by using either full FE solutions or experimental measurements of FRFs at a few intermediate steps which reduce the accumulated perturbation errors along the tool path. The proposed method is verified in five-axis milling of a thin-walled twisted fan blade. It is shown that using shell elements reduces the computation effort by ∼20 times compared to the conventional three-dimensional (3D) cube elements. The experimental calibration of the numerical model at a few discrete locations reduces the prediction error of natural frequencies by about 50%.
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      Hybrid Modeling of Position-Dependent Dynamics of Thin-Walled Parts Using Shell Elements for Milling Simulation

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    http://yetl.yabesh.ir/yetl1/handle/yetl/4283863
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    contributor authorKarimi, Behnam
    contributor authorAltintas, Yusuf
    date accessioned2022-05-08T08:23:14Z
    date available2022-05-08T08:23:14Z
    date copyright4/6/2022 12:00:00 AM
    date issued2022
    identifier issn1087-1357
    identifier othermanu_144_8_081014.pdf
    identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4283863
    description abstractThis article presents a hybrid model to update the position-dependent structural dynamic parameters of thin-walled workpieces as the metal is removed during machining. The initial workpiece is modeled by shell elements, and its full stiffness and mass matrices are used to solve the eigenvalues and mode shapes to predict the frequency response function (FRF) at a fixed location. The model is calibrated using the experimentally measured FRF, which reduces the errors contributed by the uncertainties in the material properties and damping values. The optimized finite element (FE) model is then perturbed at discrete cutting locations to obtain the updated natural frequencies and mode shapes of the part without solving the computationally prohibitive eigenvalue problem. The accuracy of the model is further improved by using either full FE solutions or experimental measurements of FRFs at a few intermediate steps which reduce the accumulated perturbation errors along the tool path. The proposed method is verified in five-axis milling of a thin-walled twisted fan blade. It is shown that using shell elements reduces the computation effort by ∼20 times compared to the conventional three-dimensional (3D) cube elements. The experimental calibration of the numerical model at a few discrete locations reduces the prediction error of natural frequencies by about 50%.
    publisherThe American Society of Mechanical Engineers (ASME)
    titleHybrid Modeling of Position-Dependent Dynamics of Thin-Walled Parts Using Shell Elements for Milling Simulation
    typeJournal Paper
    journal volume144
    journal issue8
    journal titleJournal of Manufacturing Science and Engineering
    identifier doi10.1115/1.4053596
    journal fristpage81014-1
    journal lastpage81014-17
    page17
    treeJournal of Manufacturing Science and Engineering:;2022:;volume( 144 ):;issue: 008
    contenttypeFulltext
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