The problems of metrological support for the preparation of production in machine construction

Grigoriev, S. N.; Teleshevskii, V. I.; Glubokov, A. V.; Ped, S. E.; Glubokova, S. V.

doi:10.1007/s11018-012-9993-z

Cited by 20 publications

(5 citation statements)

References 4 publications

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“…At the pre-production stage, one of the main tasks is the automation of work on metrological support [1][2][3][4]. One of the main tasks at the preparatory stage is the analysis of design documentation.…”

Section: Introductionmentioning

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: Introductionmentioning

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 use of automated systems greatly increases the productivity of work, which has a significant impact on the competitiveness of products. Automation of work on the metrological support of production lags far behind [1][2][3].…”

Section: Introductionmentioning

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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“…Form deviations are one of main characteristics defining geometric precision of parts [1][2][3][4][5][6]. The majority of cases implements the complex indices of form deviation, and the conclusion on part acceptability is made by means of comparison of value of largest straightness deviation and the tolerance.…”

Section: Introductionmentioning

confidence: 99%

Spectral Parameters of Straightness Deviation Evaluation

Glubokov

Glubokova

Shulepov

et al. 2016

MSF

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Spectral analysis of different profiles obtained during straightness deviation measurement was performed. The several profiles are showed, for which the value of straightness deviation is the same, but its behavior differs greatly. Spectral parameters characterizing the type of straightness deviation are proposed. The automated system based on factors of fuzzy-set theory with implementation in the form of neural network is developed.

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The problems of metrological support for the preparation of production in machine construction

Cited by 20 publications

References 4 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

Spectral Parameters of Straightness Deviation Evaluation

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