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Leveraging computer vision towards high-efficiency autonomous industrial facilities

Author

Listed:
  • Ibrahim Yousif

    (University of South Carolina)

  • Liam Burns

    (University of South Carolina)

  • Fadi El Kalach

    (University of South Carolina)

  • Ramy Harik

    (University of South Carolina)

Abstract

Manufacturers face two opposing challenges: the escalating demand for customized products and the pressure to reduce delivery lead times. To address these expectations, manufacturers must refine their processes, to achieve highly efficient and autonomous operations. Current manufacturing equipment deployed in several facilities, while reliable and produces quality products, often lacks the ability to utilize advancements from newer technologies. Since replacing legacy equipment may be financially infeasible for many manufacturers, implementing digital transformation practices and technologies can overcome the stated deficiencies and offer cost-affordable initiatives to improve operations, increase productivity, and reduce costs. This paper explores the implementation of computer vision, as a cutting-edge, cost-effective, open-source digital transformation technology in manufacturing facilities. As a rapidly advancing technology, computer vision has the potential to transform manufacturing operations in general, and quality control in particular. The study integrates a digital twin application at the endpoint of an assembly line, effectively performing the role of a quality officer by utilizing state-of-the-art computer vision algorithms to validate end-product assembly orientation. The proposed digital twin, featuring a novel object recognition approach, efficiently classifies objects, identifies and segments errors in assembly, and schedules the paths through the data pipeline to the corresponding robot for autonomous correction. This minimizes the need for human interaction and reduces disruptions to manufacturing operations.

Suggested Citation

  • Ibrahim Yousif & Liam Burns & Fadi El Kalach & Ramy Harik, 2025. "Leveraging computer vision towards high-efficiency autonomous industrial facilities," Journal of Intelligent Manufacturing, Springer, vol. 36(5), pages 2983-3008, June.
    • Handle: RePEc:spr:joinma:v:36:y:2025:i:5:d:10.1007_s10845-024-02396-1
    DOI: 10.1007/s10845-024-02396-1
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    References listed on IDEAS

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    1. Salomé Sanchez & Divish Rengasamy & Christopher J. Hyde & Grazziela P. Figueredo & Benjamin Rothwell, 2021. "Machine learning to determine the main factors affecting creep rates in laser powder bed fusion," Journal of Intelligent Manufacturing, Springer, vol. 32(8), pages 2353-2373, December.
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