USING MACHINE LEARNING TO PREDICT THE STRESS-STRAIN STATE OF A CIRCULAR PLATE

Abstract

Artificial neural networks are used in various areas related to information processing. For example, in such areas as: pattern recognition, optimization problems, control theory, engineering design problems, extrapolation and forecasting. There is a large amount of software that uses the capabilities of artificial neural network technology. In modern production, computer-aided design systems have become widespread, which allow to design technological processes with less time and money, with increasing accuracy of the designed processes and processing programs. The development of machine learning methods and models allows you to make quick estimates of the necessary parameters of the state of the object. From a practical point of view, machine learning models for predicting the values of structural parameters can serve as interactive assistants in the design process. One of the topical issues in the application of neural networks is their structural optimization, the choice of the optimal number of layers, neurons, activation functions and so on. In this paper, the use of machine learning to predict the stress-strain state of a circular plate is considered. An algorithm for generating circular plate parameters has been developed. A model of an artificial neural network for predicting the stress-strain state of a circular plate is constructed. The test sample, which contains the possible states of the plate depending on the geometric and mechanical parameters, was constructed using analytical formulas and the finite element method. Learning models based on artificial neural networks are built. The constructed models allow predicting the deflection in the center of the plate, as well as the maximum value of the stress intensity according to Mises. The main advantage of an artificial neural network is the speed of prediction. The calculation of the required characteristics is almost instantaneous (milliseconds). Thus, "trained" artificial neural networks can serve as interactive assistants in the design process.