Open AccessCrushing analysis of energy absorbing materials using artificial neural networks
Author(s) -
Michał Rogala,
Jakub Gajewski,
D. Głuchowski
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1736/1/012026
Subject(s) - artificial neural network , oblique case , finite element method , structural engineering , software , shear (geology) , energy analysis , computer science , energy (signal processing) , materials science , engineering , composite material , artificial intelligence , mathematics , statistics , philosophy , linguistics , programming language
This article presents the use of artificial neural networks in data analysis. The subject of the research were energy-absorbing materials under oblique loading. The forces obtained during the analysis were used to determine the crushing indicators. The numerical analysis was performed using the FEM Abaqus software. The specimens were loaded with the same force at different angles, i.e. 15, 30, 45, 60 degrees. During the numerical analyses, the normal and shear forces were measured. The tests were carried out under both static and dynamic load. On the basis of the MLP and RBF networks, analyses were carried out to study the relationship between the foam properties and the crushing efficiency indicators.