Determining surface roughness of semifinished products using computer vision and machine learning

  • Valentin Koblar, Martin Pečar, Klemen Gantar, Tea Tušar, and Bogdan Filipič. Determining surface roughness of semifinished products using computer vision and machine learning. In Proceedings of the 18th International Multiconference Information Society (IS 2015), volume A, pages 51-54, 2015.
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    In the production of components for various industries, including automotive, monitoring of surface roughness is one of the key quality control procedures since achieving appropriate surface quality is necessary for reliable functioning of the manufactured components. This study deals with the task of determining the surface roughness of semifinished products and proposes a computer-vision-based method for this purpose. To automate the design of the method, machine learning is used to induce suitable predictive models from the captured product images, and evolutionary computation to tune the computer vision algorithm parameters. The resulting method allows for accurate online determination of roughness quality classes and shows a potential for online prediction of roughness values.

    @InProceedings{2015-10-KOBLAR,
    title = {{Determining surface roughness of semifinished products using computer vision and machine learning}},
    author = {Valentin Koblar and Martin Pe\v{c}ar and Klemen Gantar and Tea Tu\v{s}ar and Bogdan Filipi\v{c}},
    booktitle = {{Proceedings of the 18th International Multiconference Information Society (IS 2015)}},
    volume = {A},
    pages = {51-54},
    date = {2015-10},
    url = {http://www.copcams.eu/wp-content/uploads/2015/10/Koblar_etal_IS2015_Vol.A_51-54.pdf},
    abstract = {In the production of components for various industries, including automotive, monitoring of surface roughness is one of the key quality control procedures since achieving appropriate surface quality is necessary for reliable functioning of the manufactured components. This study deals with the task of determining the surface roughness of semifinished products and proposes a computer-vision-based method for this purpose. To automate the design of the method, machine learning is used to induce suitable predictive models from the captured product images, and evolutionary computation to tune the computer vision algorithm parameters. The resulting method allows for accurate online determination of roughness quality classes and shows a potential for online prediction of roughness values.},
    year = {2015}
    }

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