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.

  • References

    [1] Azriel Rosenfeld and Avinash C. Kak, Digital Picture Processing, 2nd ed. New York, USA: Academic Press, 1982.

    [2] E. R. Davies, Machine Vision: Theory, Algorithms, Practicalities. London, UK: Academic Press, 1990.

    [3] Rafael C. Gonzalez and Richard E. Woods, Digital Image Processing, 3rd ed. Upper Saddle River, NJ, USA: Pearson, 2008.

    [4] Robert M. Haralick, K. Shanmugam, and I. Dinstein, “Textural features for image classification,” IEEE Transactions on Systems, Man, and Cybernetics, vol. 3, no. 6, pp. 610–621, 1973.

    [5] David H. Ballard and C. M. Brown, Computer Vision. Englewood Cliffs, NJ, USA: Prentice Hall, 1982.

    [6] Richard O. Duda, P. E. Hart, and D. G. Stork, Pattern Classification, 2nd ed. New York, USA: Wiley, 2001.

    [7] Christopher M. Bishop, Pattern Recognition and Machine Learning. New York, USA: Springer, 2006.

    [8] Vladimir Vapnik, Statistical Learning Theory. New York, USA: Wiley, 1998.

    [9] Simon Haykin, Neural Networks and Learning Machines, 3rd ed. Upper Saddle River, NJ, USA: Pearson, 2009.

    [10] Yann LeCun, Y. Bengio, and G. Hinton, “Deep learning,” Nature, vol. 521, no. 7553, pp. 436–444, 2015.

    [11] David G. Lowe, “Distinctive image features from scale-invariant keypoints,” International Journal of Computer Vision, vol. 60, no. 2, pp. 91–110, 2004.

    [12] Richard Szeliski, Computer Vision: Algorithms and Applications. London, UK: Springer, 2011.

    [13] Berthold K. P. Horn, Robot Vision. Cambridge, MA, USA: MIT Press, 1986.

    [14] IEEE Robotics and Automation Society, “Machine vision applications in industrial robotics,” IEEE Transactions on Automation Science and Engineering, various issues, 2010–2018.

    [15] Klaus Dietmayer et al., “Vision-guided robotic inspection systems for industrial automation,” IEEE Transactions on Industrial Electronics, vol. 65, no. 3, pp. 2662–2671, 2018.

  • Downloads

Similar Articles

1-10 of 83

You may also start an advanced similarity search for this article.