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High speed neuromorphic vision-based inspection of countersinks in automated manufacturing processes

Author

Listed:
  • Mohammed Salah

    (Khalifa University
    Khalifa University of Science and Technology)

  • Abdulla Ayyad

    (Khalifa University)

  • Mohammed Ramadan

    (Khalifa University)

  • Yusra Abdulrahman

    (Khalifa University
    Khalifa University)

  • Dewald Swart

    (Strata Manufacturing PJSC)

  • Abdelqader Abusafieh

    (Strata Manufacturing PJSC)

  • Lakmal Seneviratne

    (Khalifa University of Science and Technology)

  • Yahya Zweiri

    (Khalifa University
    Khalifa University)

Abstract

Countersink inspection is crucial in various automated assembly lines, especially in the aerospace and automotive sectors. Advancements in machine vision introduced automated robotic inspection of countersinks using laser scanners and monocular cameras. Nevertheless, the aforementioned sensing pipelines require the robot to pause on each hole for inspection due to high latency and measurement uncertainties with motion, leading to prolonged execution times of the inspection task. The neuromorphic vision sensor, on the other hand, has the potential to expedite the countersink inspection process, but the unorthodox output of the neuromorphic technology prohibits utilizing traditional image processing techniques. Therefore, novel event-based perception algorithms need to be introduced. We propose a countersink detection approach on the basis of event-based motion compensation and the mean-shift clustering principle. In addition, our framework presents a robust event-based circle detection algorithm to precisely estimate the depth of the countersink specimens. The proposed approach expedites the inspection process by a factor of 10 $$\times $$ × compared to conventional countersink inspection methods. The work in this paper was validated for over 50 trials on three countersink workpiece variants. The experimental results show that our method provides a standard deviation of 0.025 mm and an accuracy of 0.026 mm for countersink depth inspection despite the low resolution of commercially available neuromorphic cameras. Video Link: https://www.dropbox.com/s/pateqqwh4d605t3/final_video_new.mp4?dl=0 .

Suggested Citation

  • Mohammed Salah & Abdulla Ayyad & Mohammed Ramadan & Yusra Abdulrahman & Dewald Swart & Abdelqader Abusafieh & Lakmal Seneviratne & Yahya Zweiri, 2024. "High speed neuromorphic vision-based inspection of countersinks in automated manufacturing processes," Journal of Intelligent Manufacturing, Springer, vol. 35(7), pages 3067-3081, October.
  • Handle: RePEc:spr:joinma:v:35:y:2024:i:7:d:10.1007_s10845-023-02187-0
    DOI: 10.1007/s10845-023-02187-0
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    References listed on IDEAS

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    1. Xiaoqian Huang & Mohamad Halwani & Rajkumar Muthusamy & Abdulla Ayyad & Dewald Swart & Lakmal Seneviratne & Dongming Gan & Yahya Zweiri, 2022. "Real-time grasping strategies using event camera," Journal of Intelligent Manufacturing, Springer, vol. 33(2), pages 593-615, February.
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