Infrared and Laser Engineering, Volume. 51, Issue 12, 20220282(2022)

Mechanical-imaging comprehensive error modeling in line scan vision detection systems

Yuxuan Chen, Zhongjun Qiu, and Junjie Tang
Author Affiliations
  • State Key Laboratory of Precision Measuring Technology & Instrument, Tianjin University, Tianjin 300072, China
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    To address the problem that the accuracy of the line scan vision detection system is easily affected by the mechanical structure error and the specific influence mechanism is not clear, a mathematical model of the influence of mechanical error on the system imaging error was established and analyzed. Based on the theories of multi-system kinematics and homogeneous coordinate transformation, a mechanical system error transfer model of the line scan vision detection system was derived, and a system error comprehensive model was established with reference to the line scan imaging characteristics to clarify the correspondence between mechanical errors and system image output errors. The error sensitivity of the model was analyzed based on the complete differential-coefficient theory, and the error sources that had a great impact on the errors of the x andy dimensions of the output image were clarified. An experiment for verifying error sources is carried out and the result shows that the established system error comprehensive model can accurately identify the key error sources that have the greatest influence on the output image. The deviation between the numerical sensitivity prediction by the model and the actual value does not exceed 2.38%, which can achieve the accurate sensitivity prediction of the key error sources.

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    Yuxuan Chen, Zhongjun Qiu, Junjie Tang. Mechanical-imaging comprehensive error modeling in line scan vision detection systems[J]. Infrared and Laser Engineering, 2022, 51(12): 20220282

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    Paper Information

    Category: Photoelectric measurement

    Received: May. 20, 2022

    Accepted: --

    Published Online: Jan. 10, 2023

    The Author Email:

    DOI:10.3788/IRLA20220282

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