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    Uncertainty Propagation and Assessment of Five-Axis On-Machine Measurement Error

    Source: Journal of Manufacturing Science and Engineering:;2022:;volume( 144 ):;issue: 010::page 101004
    Author:
    Zhuang, Qixin;Wan, Neng;Guo, Yanheng;Chang, Zhiyong;Wang, Zhan
    DOI: 10.1115/1.4054286
    Publisher: The American Society of Mechanical Engineers (ASME)
    Abstract: As an important technology of adaptive machining, on-machine measurement (OMM) is always used to measure the shape of the to-be-cut surface. Due to the multi-source errors in an OMM system, the confidence of the measurement accuracy becomes particularly important before planning the next cutting procedure. OMM is implemented by multi-components involving machine tools, fixture, and on-machine probe. Each component introduces uncertainty of error into the measurement result. The positioning error, alignment error, and eccentricity error are investigated in terms of the expectation, uncertainty, and probability density function. By the kinematics of the components participated in the measurement, these errors are accumulated to affect the measurement results. Thus, the uncertainty of the final measurement result is determined by the accumulation of uncertainty of error sources. In order to evaluate the uncertainty of measurement error, an uncertainty propagation model involving positioning error of five-axis machine tools, alignment error of workpiece, and eccentricity error of the stylus tip is established in terms of the kinematic chain of OMM. Then, the uncertainty of the measurement result in vector form is obtained. Finally, the simulation and the experiment about the impeller are employed to validate the effectiveness and repeatability of the uncertainty propagation model for five-axis OMM.
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      Uncertainty Propagation and Assessment of Five-Axis On-Machine Measurement Error

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    contributor authorZhuang, Qixin;Wan, Neng;Guo, Yanheng;Chang, Zhiyong;Wang, Zhan
    date accessioned2022-12-27T23:16:13Z
    date available2022-12-27T23:16:13Z
    date copyright5/19/2022 12:00:00 AM
    date issued2022
    identifier issn1087-1357
    identifier othermanu_144_10_101004.pdf
    identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4288256
    description abstractAs an important technology of adaptive machining, on-machine measurement (OMM) is always used to measure the shape of the to-be-cut surface. Due to the multi-source errors in an OMM system, the confidence of the measurement accuracy becomes particularly important before planning the next cutting procedure. OMM is implemented by multi-components involving machine tools, fixture, and on-machine probe. Each component introduces uncertainty of error into the measurement result. The positioning error, alignment error, and eccentricity error are investigated in terms of the expectation, uncertainty, and probability density function. By the kinematics of the components participated in the measurement, these errors are accumulated to affect the measurement results. Thus, the uncertainty of the final measurement result is determined by the accumulation of uncertainty of error sources. In order to evaluate the uncertainty of measurement error, an uncertainty propagation model involving positioning error of five-axis machine tools, alignment error of workpiece, and eccentricity error of the stylus tip is established in terms of the kinematic chain of OMM. Then, the uncertainty of the measurement result in vector form is obtained. Finally, the simulation and the experiment about the impeller are employed to validate the effectiveness and repeatability of the uncertainty propagation model for five-axis OMM.
    publisherThe American Society of Mechanical Engineers (ASME)
    titleUncertainty Propagation and Assessment of Five-Axis On-Machine Measurement Error
    typeJournal Paper
    journal volume144
    journal issue10
    journal titleJournal of Manufacturing Science and Engineering
    identifier doi10.1115/1.4054286
    journal fristpage101004
    journal lastpage101004_20
    page20
    treeJournal of Manufacturing Science and Engineering:;2022:;volume( 144 ):;issue: 010
    contenttypeFulltext
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