Mechanics of Deformable Solids
Original article
http://doi.org/10.24866/2227-6858/2022-3/3-11

Morkovin A., Veyna V.

ANDREY V. MORKOVIN, Candidate of Engineering Sciences, Associate Professor, morkovin_av@dvfu.ru
VITALIY S. VEYNA, Engineer, veina.vs@students.dvfu.ru
Polytechnic Institute
Far Eastern Federal University
Vladivostok, Russia

Development of a methodology for solving problems of predicting the deformed shape of a plate under load using neural networks

Abstract. A methodology for solving problems of predicting the deformed shape of a plate using neural networks has been developed. The application of CAD/CAE systems for research and obtaining empirical data of stress-strain state of structures, as well as for training neural networks is considered. The developed methodology has been tested on a workpiece presented in the form of a plate. As a result of neural networks training, the following data were obtained: data for determination of displacements of the points of deformed surface of the plate by pre-known coordinates of the loads application; data for determination the coordinates of the points of loads application by the pre-known points displacements on the plate surface. The results of forecasts of neural networks with empirical data are analyzed.

Keywords: neural networks, machine learning, plate deformation, CAD/CAE systems, production automation

See the reference in English at the end of the article

For citation: Morkovin A., Veyna V. Development of a methodology for solving problems of predicting the deformed shape of a plate under load using neural networks. FEFU: School of Engineering Bulletin. 2022;(3):3-11. (In Russ.).