Geodesic
Automatic estimation of defects in composite structures as disturbances based on machine learning classifiers oriented mathematical models with uncertainties
Informacionnye tehnologii i vyčislitelnye sistemy, no. 3 (2020), pp. 13-29 Cet article a éte moissonné depuis la source Math-Net.Ru

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The proposed system for detecting defects in composite materials, such as carbon fiber or textile fabric, is described. Defects in the structure of the product are detected using a computer vision system based on optical sensors, i.e. a visual inspection is carried out — a necessary stage in the production process of composite materials. The difference of the proposed method from the existing solutions is the unique model of the sensor-material interaction and its strict mathematical description. A comparison with the reference model of the structure specified analytically is used.
Keywords: mathematical modeling, flaw detection, sensors, machine learning.
Mots-clés : inspection, composites 
@article{ITVS_2020_3_a1,
     author = {A. A. Zhilenkov and S. G. Cherny},
     title = {Automatic estimation of defects in composite structures as disturbances based on machine learning classifiers oriented mathematical models with uncertainties},
     journal = {Informacionnye tehnologii i vy\v{c}islitelnye sistemy},
     pages = {13--29},
     year = {2020},
     number = {3},
     language = {en},
     url = {http://geodesic.mathdoc.fr/item/ITVS_2020_3_a1/}
}
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A. A. Zhilenkov; S. G. Cherny. Automatic estimation of defects in composite structures as disturbances based on machine learning classifiers oriented mathematical models with uncertainties. Informacionnye tehnologii i vyčislitelnye sistemy, no. 3 (2020), pp. 13-29. http://geodesic.mathdoc.fr/item/ITVS_2020_3_a1/