title: ÐÑÐµÐ½ÐºÐ° ÑÑÑÐµÐºÑÐ¸Ð²Ð½ÑÑ Ñ Ð°ÑÐ°ÐºÑÐµÑÐ¸ÑÑÐ¸Ðº Ð¿Ð¾Ð»Ð¸Ð¼ÐµÑÐ½ÑÑ ÐºÐ¾Ð¼Ð¿Ð¾Ð·Ð¸ÑÐ½ÑÑ Ð¼Ð°ÑÐµÑÐ¸Ð°Ð»Ð¾Ð² Ñ Ð¿ÑÐ¸Ð¼ÐµÐ½ÐµÐ½Ð¸ÐµÐ¼ Ð¼ÐµÑ Ð°Ð½Ð¸ÑÐµÑÐºÐ¸ Ð¼Ð¾ÑÐ¸Ð²Ð¸ÑÐ¾Ð²Ð°Ð½Ð½ÑÑ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½ÑÑ ÑÐµÑÐµÐ¹
title: Ð²ÑÐ¿ÑÑÐºÐ½Ð°Ñ ÐºÐ²Ð°Ð»Ð¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¾Ð½Ð½Ð°Ñ ÑÐ°Ð±Ð¾ÑÐ° Ð±Ð°ÐºÐ°Ð»Ð°Ð²ÑÐ°
titleType: Subtitle
title: Evaluation of effective characteristics of polymer composite materials using mechanically motivated neural networks
Creators: name: ÐÐ¸ÐºÐ¸ÑÐ¸Ð½, ÐÐ½Ð´ÑÐµÐ¹
Publisher: name: Ð¡Ð°Ð½ÐºÑ-ÐÐµÑÐµÑÐ±ÑÑÐ³ÑÐºÐ¸Ð¹ Ð¿Ð¾Ð»Ð¸ÑÐµÑÐ½Ð¸ÑÐµÑÐºÐ¸Ð¹ ÑÐ½Ð¸Ð²ÐµÑÑÐ¸ÑÐµÑ ÐÐµÑÑÐ° ÐÐµÐ»Ð¸ÐºÐ¾Ð³Ð¾
Year: 2024
Subjects: ÑÑÑÐµÐºÑÐ¸Ð²Ð½ÑÐµ ÑÐ¿ÑÑÐ³Ð¸Ðµ ÑÐ²Ð¾Ð¹ÑÑÐ²Ð°
Ð²Ð¾Ð»Ð¾ÐºÐ½Ð¸ÑÑÑÐµ ÐºÐ¾Ð¼Ð¿Ð¾Ð·Ð¸ÑÐ¸Ð¾Ð½Ð½ÑÐµ Ð¼Ð°ÑÐµÑÐ¸Ð°Ð»Ñ
ÐºÐ¾Ð¼Ð¿Ð¾Ð·Ð¸ÑÐ¸Ð¾Ð½Ð½ÑÐµ Ð¼Ð°ÑÐµÑÐ¸Ð°Ð»Ñ
Ð¿ÑÐ»ÑÑÑÐ·Ð¸Ð¾Ð½Ð½ÑÐµ Ð¿ÑÐ¾ÑÐ¸Ð»Ð¸
Ð¼ÐµÑÐ¾Ð´ ÐºÐ¾Ð½ÐµÑÐ½Ð¾-ÑÐ»ÐµÐ¼ÐµÐ½ÑÐ½Ð¾Ð¹ Ð³Ð¾Ð¼Ð¾Ð³ÐµÐ½Ð¸Ð·Ð°ÑÐ¸Ð¸
Ansys
APDL
PyTorch
Ð½ÐµÐ¹ÑÐ¾Ð½Ð½ÑÐµ ÑÐµÑÐ¸
efficient elastic properties
fiber-reinforced composite materials
composite materials
pultruded profiles
finite element homogenization method
neural networks

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description: This work considers the problem of finding the effective properties of unidirectional fibrous composite materials using neural network technologies. The first chapter presents general information about polymer composite materials, including information about their chemical composition, material structure, mechanical properties, as well as the manufacturing process and areas of application. The second chapter describes the main analytical methods used to determine the effective properties of materials. These methods allow predicting and evaluating the characteristics of composite materials based on their composition and structure, and also investigate the influences of individual material properties on the accuracy of the considered approaches. The third chapter is devoted to the method of finite element homogenization, including an analysis of the influence of geometry on effective properties using finite element packages Ansys Material Designer and APDL. The fourth chapter describes the process of building a neural network for predicting the effective properties of composite materials using the PyTorch library, using data on the composition and structure of materials, as well as the results of analytical methods and the method of finite element homogenization from previous chapters. In addition, the possibilities of improving the prediction of composite material properties, taking into account information about their microstructure, are considered. The result of the work is a mechanically motivated neural network that allows modeling the behavior of fibrous composite materials.