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Applying Computer Vision to Track Tool Movement in an Automotive Assembly Plant

Published: 18 April 2019 Publication History

Abstract

In the context of automobile assembly, torque tools are used by technicians to mount bolts to parts of a vehicle. Different bolts require varying torque levels to be fastened correctly. An intelligent tool can be programmed to deliver torque levels in a specified order that must be strictly followed by the technician as they secure the bolts. Because of the pre-specified order, it is imperative that the technician follows the correct order precisely. This paper describes our investigation into the use of computer vision to identify, correct, and document the human error involved in the bolt securing process (i.e., incorrect ordering) on an automotive factory assembly line. We built a computer vision application to select a desired order of bolts, detect visitation of a torque tool to each bolt location, and report errors made in the sequence of actions in a vehicle assembly line. The application is used to determine the accuracy of error detection for varying degrees of distance between bolt locations and sizes in order to determine which assembly line stations are appropriate for this type of monitoring. The context for application of our project is a large automotive manufacturing facility in the Southeastern United States -- the Mercedes-Benz US International (MBUSI) factory in Vance, Alabama. The limitations of computer vision for determining errors in assembly order using the torque tool are reported with a discussion of lessons learned.

References

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A. Kadambi, A. Bhandari, and R. Raskar, "3D Depth Cameras in Vision: Benefits and Limitations of the Hardware with an Emphasis on the First- and Second-Generation Kinect Models," in Computer Vision & Machine Learning with RGB-D Sensors, 2014, pp. 3--26.
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H. Sarbolandi, D. Lefloch, and A. Kolb, "Kinect Range Sensing: Structured-Light versus Time-of-Flight Kinect," Computer Vision and Image Understanding, 2015, vol. 139, pp. 1--20.
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J. F. Henriques, R. Caseiro, P. Martins, and J. Batista, "High-Speed Tracking with Kernelized Correlation Filters," IEEE Transactions on Pattern Analysis and Machine Intelligence, 2015, vol. 37, no. 3, pp. 583--596.
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P. A. Viola and M. J. Jones, "Rapid Object Detection using a Boosted Cascade of Simple Features," Conference on Computer Vision and Pattern Recognition, 2001, pp. 511--518.

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  1. Applying Computer Vision to Track Tool Movement in an Automotive Assembly Plant

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    cover image ACM Conferences
    ACMSE '19: Proceedings of the 2019 ACM Southeast Conference
    April 2019
    295 pages
    ISBN:9781450362511
    DOI:10.1145/3299815
    • Conference Chair:
    • Dan Lo,
    • Program Chair:
    • Donghyun Kim,
    • Publications Chair:
    • Eric Gamess
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 18 April 2019

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    Author Tags

    1. Automobile Assembly
    2. Computer Vision
    3. Machine Learning Classification
    4. Object Detection

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    • Short-paper
    • Research
    • Refereed limited

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    ACM SE '19
    Sponsor:
    ACM SE '19: 2019 ACM Southeast Conference
    April 18 - 20, 2019
    GA, Kennesaw, USA

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    Overall Acceptance Rate 402 of 779 submissions, 52%

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    • (2025)Towards cognition-augmented human-centric assemblyRobotics and Computer-Integrated Manufacturing10.1016/j.rcim.2024.10285291:COnline publication date: 1-Feb-2025
    • (2024)Responsible manufacturing toward Industry 5.0Manufacturing from Industry 4.0 to Industry 5.010.1016/B978-0-443-13924-6.00008-9(231-263)Online publication date: 2024
    • (2023)Computer Vision Techniques in ManufacturingIEEE Transactions on Systems, Man, and Cybernetics: Systems10.1109/TSMC.2022.316639753:1(105-117)Online publication date: Jan-2023
    • (2023)A technology maturity assessment framework for Industry 5.0 machine vision systems based on systematic literature review in automotive manufacturingInternational Journal of Production Research10.1080/00207543.2023.2270588(1-37)Online publication date: 17-Oct-2023
    • (2023)Rapid offline detection and 3D annotation of assembly elements in the augmented assemblyExpert Systems with Applications: An International Journal10.1016/j.eswa.2023.119839222:COnline publication date: 15-Jul-2023
    • (2022)Smart connected worker edge platform for smart manufacturing: Part 2—Implementation and on‐site deployment case studyJournal of Advanced Manufacturing and Processing10.1002/amp2.101304:4Online publication date: 14-Jun-2022
    • (2021)The Role of Machine Vision in Industry 4.0: an automotive manufacturing perspective2021 IEEE International Conference on Imaging Systems and Techniques (IST)10.1109/IST50367.2021.9651453(1-6)Online publication date: 24-Aug-2021
    • (2021)Measuring the effect of automatically authored video aid on assembly time for procedural knowledge transfer among operators in adaptive assembly stationsInternational Journal of Production Research10.1080/00207543.2021.197085061:12(3910-3925)Online publication date: 6-Sep-2021
    • (undefined)Rapid Offline Detection and 3d Annotation of Assembly Elements in the Augmented AssemblySSRN Electronic Journal10.2139/ssrn.4052362

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