Stability Analysis System of Functionally Graded Material Beams Based on Machine Learning
Year: 2025
Type: conference-paper
Abstract: Functionally graded material(FGM) is a kind of non-uniform material whose mechanical properties change continuously in space, and there are some problems of stress concentration and transfer at the interface. This non-uniformity brings great challenges to the stability analysis of beams. At present, the numerical simulation methods applicable to FGM beams are not mature enough and need to be further developed and improved. The purpose of this paper is to solve the existing problems by applying deep neural network in machine learning to establish a stability analysis system of functionally graded material beams. In this paper, the stability of material beam and the performance of machine learning analysis system are compared and verified by means of experiment and numerical simulation. The experimental results show that the performance of the stability analysis module is 0.8254, 0.8679, 0.7956 and 0.8193, respectively.
Source: Procedia Computer Science
Authors: Jie Wang, Wanqing Chen, Xiaoying Wang, Nana Liu
Institution: Chongqing College of Architecture and Technology
Language: English
Subfield: Mechanics of Materials
Field: Engineering
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4411175360