Spectral Parameters of Straightness Deviation Evaluation

Glubokov, A. V.; Glubokova, S. V.; Shulepov, A. V.; Ped, S. E.

doi:10.4028/www.scientific.net/msf.876.74

Cited by 3 publications

(2 citation statements)

References 13 publications

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“…Knowing the coordinates of text blocks containing the values of shape and location deviations, it is possible to localize the search area for shape and location deviation symbols, since the type of deviation is always located to the left of the value, which will significantly increase work productivity (Figure 5). For this, it is necessary to use a convolutional neural network [15,16], the structure of which is shown in Figure 6. When a location deviation is identified, another associated block will appear containing the designation of the base element.…”

Section: Proposed Methodsmentioning

confidence: 99%

Algorithm for the automated identification of the accuracy requirements set in the detail drawing

Glubokov

Shulepov²,

Ananev³

et al. 2022

Optical Metrology and Inspection for Industrial Applications IX

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The article develops an approach to automated identification of the accuracy requirements set in the detail drawing. A technique for recognizing accuracy requirements based on image analysis is proposed. The algorithm for identifying tolerances on linear sizes is based on classical text recognition algorithms. The advantage of the developed approach is its versatility. The effectiveness of recognizing tolerances on linear sizes does not depend on the options for setting and orientation of text entries in the drawing. A database of tolerances on linear sizes has been developed, which makes it possible to increase the efficiency of identifying accuracy requirements by comparing recognition results with standard values. The structure of a convolutional neural network for identify the symbols of tolerances of form, orientation, location and run-out, roughness, is proposed. This makes it possible to determine with high accuracy the area of requirements and improve identification performance

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Section: Proposed Methodsmentioning

confidence: 99%

Algorithm for the automated identification of the accuracy requirements set in the detail drawing

Glubokov

Shulepov²,

Ananev³

et al. 2022

Optical Metrology and Inspection for Industrial Applications IX

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“…In accordance with regulatory documents, the adjacent plane should be used as a reference. Studies [13,14,17,18] show that the value of the error caused by the choice of the reference base can reach 50%, which is unacceptable in most cases. The construction of the adjacent plane is reduced to solving the minimax problem:…”

Section: Determination Of Flatness Valuementioning

confidence: 99%

Automated measuring system for straightness and flatness deviations of extended surfaces

Glubokov¹,

Glubokova²,

Afonina³

et al. 2022

Optical Metrology and Inspection for Industrial Applications IX

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The article developed an approach to improve the accuracy of measuring deviations from straightness and flatness of extended surfaces. One of the most accurate and productive methods for measuring deviations from straightness and flatness of extended surfaces is the step method using electronic levels. The main difficulty of the measurement lies in the correct positioning of the electronic level on the measured surface. Algorithms for processing the results of measurements assume the coincidence of the measured points during successive measurements. Otherwise, a significant methodological error is introduced. A technique for recognizing the position of the level on the measured surface is proposed based on the analysis of images obtained from video cameras. The advantage of the algorithm is its resistance to determining the position of the level, regardless of its orientation on the measured surface. Software has been developed to correct the position of the level on the measured surface. An algorithm for determining the adjacent plane based on the gradient descent method is proposed. The dependences of the methodological measurement error on the number of measured points and the accuracy of their determination are revealed.

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Research of the Influence of Surface Color on the Measurement Error of Parameter Geometric Accuracy in Laser Scanning Optoelectronic Measurement Information Systems

Win

Shulepov

Emel'yanov

et al. 2019

2019 IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering (EIConRus)

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Spectral Parameters of Straightness Deviation Evaluation

Cited by 3 publications

References 13 publications

Algorithm for the automated identification of the accuracy requirements set in the detail drawing

Algorithm for the automated identification of the accuracy requirements set in the detail drawing

Automated measuring system for straightness and flatness deviations of extended surfaces

Research of the Influence of Surface Color on the Measurement Error of Parameter Geometric Accuracy in Laser Scanning Optoelectronic Measurement Information Systems

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