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Error analysis based on error transfer theory and compensation strategy for LED chip visual localization systems

Author

Listed:
  • Diyi Zhou

    (Huazhong University of Science and Technology)

  • Shihua Gong

    (Huazhong University of Science and Technology)

  • Ziyue Wang

    (Huazhong University of Science and Technology)

  • Delong Li

    (Huazhong University of Science and Technology)

  • Huaiqing Lu

    (Huazhong University of Science and Technology)

Abstract

In the manufacturing process of LED chips, the accuracy of the LED chip visual localization system affects the quality of LED chip production directly. There are many errors that have impacts on positioning system accuracy. Therefore, the identification and compensation of the critical errors is key to efficiently improving the precision of the system. Based on this fact, an error analysis method and an error compensation strategy are proposed in this paper. The first step was to measure the relevant error sources that may affect the localization system. Then error model of localization system was established, and the validity of this error model was verified by comparing simulated and actual positioning results. In addition, the impact factors of each error source on localization system accuracy were obtained using the error transfer theory. According to the error analysis results, an efficient error compensation strategy was proposed, which could compensate the errors in order of impact factors, and judge whether the error compensation method is optimal. Finally, the experimental results proved that the proposed error analysis method was valid and the error compensation strategy could efficiently enhance the positioning accuracy to meet the industrial application requirements.

Suggested Citation

  • Diyi Zhou & Shihua Gong & Ziyue Wang & Delong Li & Huaiqing Lu, 2021. "Error analysis based on error transfer theory and compensation strategy for LED chip visual localization systems," Journal of Intelligent Manufacturing, Springer, vol. 32(5), pages 1345-1359, June.
    • Handle: RePEc:spr:joinma:v:32:y:2021:i:5:d:10.1007_s10845-020-01615-9
    DOI: 10.1007/s10845-020-01615-9
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    References listed on IDEAS

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    1. Hui Lin & Bin Li & Xinggang Wang & Yufeng Shu & Shuanglong Niu, 2019. "Automated defect inspection of LED chip using deep convolutional neural network," Journal of Intelligent Manufacturing, Springer, vol. 30(6), pages 2525-2534, August.
    2. Chien-Chang Hsu & Min-Sheng Chen, 2016. "Intelligent maintenance prediction system for LED wafer testing machine," Journal of Intelligent Manufacturing, Springer, vol. 27(2), pages 335-342, April.
    3. Chung-Feng Jeffrey Kuo & Chun-Ping Tung & Wei-Han Weng, 2019. "Applying the support vector machine with optimal parameter design into an automatic inspection system for classifying micro-defects on surfaces of light-emitting diode chips," Journal of Intelligent Manufacturing, Springer, vol. 30(2), pages 727-741, February.
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