Overview of Methods for Assessing and Predicting Surface Texture Using Artificial Neural Network Technologies

The article presents an analysis of the use of machine learning technologies in the engineering industry. Specifically, the article provides an overview and systematization of studies on methods for assessing roughness parameters of machined surface texture using a combination of computer vision methods and artificial neural networks. Specific examples of the use of non-contact technologies for examining machined surfaces are considered, their advantages and disadvantages are outlined.

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Publication Details

Journal
Russian Engineering Research
Published
2025-12-01
DOI
https://doi.org/10.3103/s1068798x25702879
Primary Topic
Surface Roughness and Optical Measurements
Type
article
Field-Weighted Citation Impact
0.00
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article

Overview of Methods for Assessing and Predicting Surface Texture Using Artificial Neural Network Technologies

M. A. Glazunov, A. V. Popov
Russian Engineering Research
Surface Roughness and Optical Measurements
article

Overview of Methods for Assessing and Predicting Surface Texture Using Artificial Neural Network Technologies

M. A. Glazunov, A. V. Popov
article en

Abstract

The article presents an analysis of the use of machine learning technologies in the engineering industry. Specifically, the article provides an overview and systematization of studies on methods for assessing roughness parameters of machined surface texture using a combination of computer vision methods and artificial neural networks. Specific examples of the use of non-contact technologies for examining machined surfaces are considered, their advantages and disadvantages are outlined.

Russian Engineering ResearchVol. 45(12)
Russian Academy of Sciences (RU), Lomonosov Moscow State University (RU), Samara Federal Research Scientific Center (RU)
Industry, innovation and infrastructure
Openalex Percentile: Top 31%
Surface Roughness and Optical Measurements
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