Multi-axed phase-transforming cellular material: A data-driven design and validation using finite-element method and machine learning

Okugawa, Masayuki; Kanegae, Sosuke; Koizumi, Yuichiro

Extreme Mechanics Letters, 2025, 77, 102319

Number of Access:325（2025-08-15 06:46 Counts）

Identifier to cite or link to this item: https://hdl.handle.net/11094/101105

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Title
Multi-axed phase-transforming cellular material: A data-driven design and validation using finite-element method and machine learning
Creator
NRID
1000070847160 1000070847160
ORCID
0000-0002-6844-6856 0000-0002-6844-6856
researchmap permalink
masayuki.okugawa masayuki.okugawa
Scopus Author ID
57202824128 57202824128
CreatorName
Okugawa, Masayuki
CreatorName
Kanegae, Sosuke
NRID
1000010322174 1000010322174
ORCID
0000-0001-7061-7135 0000-0001-7061-7135
researchmap permalink
YuichiroKoizumi YuichiroKoizumi
Scopus Author ID
57202772340 57202772340
CreatorName
Koizumi, Yuichiro
Subject
Mechanical metamaterial
Phase-transformation cellular material
3D printing
Bistability
Atom-mimetic
Description
Okugawa M., Kanegae S., Koizumi Y.. Multi-axed phase-transforming cellular material: A data-driven design and validation using finite-element method and machine learning. Extreme Mechanics Letters 77, 102319 (2025); https://doi.org/10.1016/j.eml.2025.102319.
Abstract
We developed the novel Atom-Mimetic Cube-Diagonally Multi-Axed Phase-Transforming Cellular Material (AMCDMA-PXCM), hereafter AM-PXCM for short, for a multi-axial bistable metamaterial designed with inspiration from a face-centered cubic (FCC) crystal structure: the designed AM-PXCM consists of spheres at atomic positions of structure and dogleg-shaped beams connecting nearest neighbor spheres. Stress-strain relationship of AM-PXCM was investigated by Finite Element Method (FEM) simulation. Analyzing the results by Logistic classification revealed that the mechanical properties significantly depend on the designing parameters and the distance between the beam and the tetrahedron (k) dominantly determines the bistability of the FCC-based AM-PXCM. In addition, combined with the machine learning method (i.e., inverse design), we succeeded to predict the designing parameters to have the desired mechanical properties for a bistable metamaterial. The designed AM-PXCMs were realized using a 3D printer and validified to show the predicted mechanical properties. This established method for developing AM-PXCM is suggested to be also applied to a development of an AM-PXCM with the symmetry of other crystal structures.
Publisher
Elsevier Ltd
Source Title
Extreme Mechanics Letters
Volume (Issue)
77
Page
102319
Date of Issued
2025-03-26
Language
English
Handle URL
https://hdl.handle.net/11094/101105
eISSN
23524316
Relation.isIdenticalTo
DOI (Publisher Version)
https://doi.org/10.1016/j.eml.2025.102319
Access Rights
open access
CopyRight
This article is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
oaire:version
Version of Record
Category
学術雑誌論文 Journal Article