MODELING OF POROUS METAL DEFORMATION BY SPH METHOD AND CONSTRUCTION OF CONSTITUTIVE EQUATIONS USING ARTIFICIAL NEURAL NETWORKS

This study aims to develop a macroscopic continuum model of dynamic deformation of porous metals based on the application of artificial neural networks. Data sets obtained by modeling the compression of representative volumes of porous media by smoothed particle hydrodynamics based on a dislocation plasticity model previously parameterized for copper are used to train the ANN. Such modeling is used both for training data sets and to investigate the deformation physics of porous copper with micrometer and millimeter scale pores.

Authors

Institutions

Publication Details

Journal
Челябинский физико-математический журнал
Published
2025-03-27
DOI
https://doi.org/10.47475/2500-0101-2025-10-1-158-173
Primary Topic
Metallurgy and Material Forming
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

MODELING OF POROUS METAL DEFORMATION BY SPH METHOD AND CONSTRUCTION OF CONSTITUTIVE EQUATIONS USING ARTIFICIAL NEURAL NETWORKS

Fanil T. Latypov, Egor S. Rodionov, Alexander E. Mayer, P. A. Bezborodova
Челябинский физико-математический журнал
Metallurgy and Material Forming
article

MODELING OF POROUS METAL DEFORMATION BY SPH METHOD AND CONSTRUCTION OF CONSTITUTIVE EQUATIONS USING ARTIFICIAL NEURAL NETWORKS

Fanil T. Latypov, Egor S. Rodionov, Alexander E. Mayer, P. A. Bezborodova
article en

Abstract

This study aims to develop a macroscopic continuum model of dynamic deformation of porous metals based on the application of artificial neural networks. Data sets obtained by modeling the compression of representative volumes of porous media by smoothed particle hydrodynamics based on a dislocation plasticity model previously parameterized for copper are used to train the ANN. Such modeling is used both for training data sets and to investigate the deformation physics of porous copper with micrometer and millimeter scale pores.

Челябинский физико-математический журналVol. 10(1)
Chelyabinsk State University (RU)
Openalex Percentile: Top 93%
Metallurgy and Material Forming
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.