Machine Vision-Based Quality Inspection in Robotic Manufacturing Lines

  • Authors

    • Louis Pouzin Computer Network Researcher, IRIA, France Author
    • Jacques Arsac Professor of Computer Science, University of Paris, France Author

    DOI:

    https://doi.org/10.67228/30715725/IJIARE-2019PI1M8K

    Published 02-05-2019

  • Machine Vision, Robotic Manufacturing, Quality Inspection, Industrial Automation, Image Processing, Defect Detection, Pattern Recognition, Smart Manufacturing

    Issue

    Vol. 2 No. 1 (2019)

    Section

    Articles

    How to Cite

    [1]
    L. Pouzin and J. Arsac, “Machine Vision-Based Quality Inspection in Robotic Manufacturing Lines”, IJIARE, vol. 2, no. 1, pp. 01–14, Feb. 2019, doi: 10.67228/30715725/IJIARE-2019PI1M8K.

  • Abstract

    Machine vision has become a key technology in modern industrial automation, enabling fully automated quality inspection in robotic manufacturing systems. Unlike traditional human inspection methods, machine vision offers higher accuracy, consistency, speed, and lower operational costs. These systems use cameras, lighting, lenses, image processing, pattern recognition, and AI techniques to detect defects, verify dimensions, classify products, and monitor manufacturing processes in real time. This survey reviews machine vision-based quality inspection methods in robotic manufacturing up to 2019, covering major applications in industries such as automotive, electronics, aerospace, pharmaceuticals, and food processing. It highlights advancements in feature extraction, defect detection algorithms, classification models, and robotic integration frameworks. The study shows that machine vision significantly improves inspection accuracy, reduces cycle time, and enhances manufacturing consistency, while also discussing challenges such as illumination changes, computational complexity, and system adaptability. Overall, machine vision plays a vital role in smart manufacturing and Industry 4.0 production systems.

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