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

Mohammed Salah (), Abdulla Ayyad, Mohammed Ramadan, Yusra Abdulrahman, Dewald Swart, Abdelqader Abusafieh, Lakmal Seneviratne and Yahya Zweiri
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Mohammed Salah: Khalifa University
Abdulla Ayyad: Khalifa University
Mohammed Ramadan: Khalifa University
Yusra Abdulrahman: Khalifa University
Dewald Swart: Strata Manufacturing PJSC
Abdelqader Abusafieh: Strata Manufacturing PJSC
Lakmal Seneviratne: Khalifa University of Science and Technology
Yahya Zweiri: Khalifa University

Journal of Intelligent Manufacturing, 2024, vol. 35, issue 7, No 4, 3067-3081

Abstract: 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 .

Keywords: Neuromorphic vision; Machine vision; Countersink inspection; Robotic automation; Precision manufacturing (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s10845-023-02187-0

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