Neural network technology for identifying defect sizes in half-plane based on time and positional scanning
Introduction. The selected research topic urgency is due to the need for a quick assessment of the condition and reliability of materials used in various designs. The work objective was to study parameters of the influence of the defect on the response of the surface of the medium to the shock effec...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | Russian |
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Don State Technical University
2020-10-01
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| Series: | Advanced Engineering Research |
| Subjects: | |
| Online Access: | https://www.vestnik-donstu.ru/jour/article/view/1685 |
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| author | A. N. Solov'ev A. V. Cherpakov P. V. Vasil’ev I. A. Parinov E. V. Kirillova |
| author_facet | A. N. Solov'ev A. V. Cherpakov P. V. Vasil’ev I. A. Parinov E. V. Kirillova |
| author_sort | A. N. Solov'ev |
| collection | DOAJ |
| description | Introduction. The selected research topic urgency is due to the need for a quick assessment of the condition and reliability of materials used in various designs. The work objective was to study parameters of the influence of the defect on the response of the surface of the medium to the shock effect. The solution to the inverse problem of restoring the radius of a defect is based on the combination of a computational approach and the use of artificial neural networks (ANN). The authors have developed a technique for restoring the parameters of a defect based on the computational modeling and ANN. Materials and Methods. The problem is solved in the flat setting through the finite element method (FEM). In this paper, we used the linear equations of the elasticity theory with allowance for energy dissipation. The finite element method implemented in the ANSYS package was used as a method for solving the boundary value problem. MATLAB complex was used as a simulation of the application process (ANN). Results. A finite element model of a layered structure has been developed in a flat formulation of the problem in the ANSYS package. The problem of determining unsteady vibrations under pulsed loading for different radius variations of the defect is solved. Positional scanning of the research object is applied. Graphical dependences of the vibration amplitudes of points on the surface on the defect radius are plotted. Discussion and Conclusions. As a result of studying the dependences of vibration responses on the defect radius, the authors have developed an approach to restore this parameter in a flat structure based on a combination of the FEM and ANN. The research has shown that the amount of data used is sufficient for successful training of the constructed ANN model and identification of a hidden defect in the structure. |
| first_indexed | 2024-04-10T03:16:59Z |
| format | Article |
| id | doaj.art-e6551ccabc1c4c3185bb7d71ed02f7ee |
| institution | Directory Open Access Journal |
| issn | 2687-1653 |
| language | Russian |
| last_indexed | 2025-03-14T10:27:01Z |
| publishDate | 2020-10-01 |
| publisher | Don State Technical University |
| record_format | Article |
| series | Advanced Engineering Research |
| spelling | doaj.art-e6551ccabc1c4c3185bb7d71ed02f7ee2025-03-02T11:04:28ZrusDon State Technical UniversityAdvanced Engineering Research2687-16532020-10-0120320521510.23947/2687-1653-2020-20-3-205-2151474Neural network technology for identifying defect sizes in half-plane based on time and positional scanningA. N. Solov'ev0A. V. Cherpakov1P. V. Vasil’ev2I. A. Parinov3E. V. Kirillova4Don State Technical University; Southern Federal UniversityDon State Technical University; Southern Federal UniversityDon State Technical UniversitySouthern Federal UniversityRheinMain University of Applied SciencesIntroduction. The selected research topic urgency is due to the need for a quick assessment of the condition and reliability of materials used in various designs. The work objective was to study parameters of the influence of the defect on the response of the surface of the medium to the shock effect. The solution to the inverse problem of restoring the radius of a defect is based on the combination of a computational approach and the use of artificial neural networks (ANN). The authors have developed a technique for restoring the parameters of a defect based on the computational modeling and ANN. Materials and Methods. The problem is solved in the flat setting through the finite element method (FEM). In this paper, we used the linear equations of the elasticity theory with allowance for energy dissipation. The finite element method implemented in the ANSYS package was used as a method for solving the boundary value problem. MATLAB complex was used as a simulation of the application process (ANN). Results. A finite element model of a layered structure has been developed in a flat formulation of the problem in the ANSYS package. The problem of determining unsteady vibrations under pulsed loading for different radius variations of the defect is solved. Positional scanning of the research object is applied. Graphical dependences of the vibration amplitudes of points on the surface on the defect radius are plotted. Discussion and Conclusions. As a result of studying the dependences of vibration responses on the defect radius, the authors have developed an approach to restore this parameter in a flat structure based on a combination of the FEM and ANN. The research has shown that the amount of data used is sufficient for successful training of the constructed ANN model and identification of a hidden defect in the structure.https://www.vestnik-donstu.ru/jour/article/view/1685flat layered structuredefectnon-destructive diagnosticsfe modelingimpulse actionunsteady oscillationssurface wavesartificial neural networkspositional scanningamplitude-time characteristics |
| spellingShingle | A. N. Solov'ev A. V. Cherpakov P. V. Vasil’ev I. A. Parinov E. V. Kirillova Neural network technology for identifying defect sizes in half-plane based on time and positional scanning Advanced Engineering Research flat layered structure defect non-destructive diagnostics fe modeling impulse action unsteady oscillations surface waves artificial neural networks positional scanning amplitude-time characteristics |
| title | Neural network technology for identifying defect sizes in half-plane based on time and positional scanning |
| title_full | Neural network technology for identifying defect sizes in half-plane based on time and positional scanning |
| title_fullStr | Neural network technology for identifying defect sizes in half-plane based on time and positional scanning |
| title_full_unstemmed | Neural network technology for identifying defect sizes in half-plane based on time and positional scanning |
| title_short | Neural network technology for identifying defect sizes in half-plane based on time and positional scanning |
| title_sort | neural network technology for identifying defect sizes in half plane based on time and positional scanning |
| topic | flat layered structure defect non-destructive diagnostics fe modeling impulse action unsteady oscillations surface waves artificial neural networks positional scanning amplitude-time characteristics |
| url | https://www.vestnik-donstu.ru/jour/article/view/1685 |
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