Design exploration of additively manufactured chiral auxetic structure using explainable machine learning

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21260%2F23%3A00379364" target="_blank" >RIV/68407700:21260/23:00379364 - isvavai.cz</a>

Result on the web

<a href="https://doi.org/10.1016/j.matdes.2023.112128" target="_blank" >https://doi.org/10.1016/j.matdes.2023.112128</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.matdes.2023.112128" target="_blank" >10.1016/j.matdes.2023.112128</a>

Result language

angličtina

Original language name

Design exploration of additively manufactured chiral auxetic structure using explainable machine learning

Original language description

A design exploration of hexachiral structures using explainable machine learning (ML) is performed in this work. The hexachiral structures are fabricated using resin via vat photopolymerization (VPP). The ML model is used to build the function that explains the association between Poisson’s ratio and the hexachiral design parameters. The data set for ML model construction is first collected by using the Halton sequence and simulated using the finite element method (FEM). To validate the data set, the results obtained from the FEM simulation are compared with those obtained from the compression test. The Gaussian Process Regression (GPR) models for Poisson’s ratio and porosity are constructed to extract important design insight. A Global Sensitivity Analysis (GSA) and Shapley Additive Explanations (SHAP) are used to analyze the sensitivity of the porosity and Poisson’s ratio to the hexachiral design parameters. GSA result shows that the strut’s thickness is the most decisive parameter that affects the Poisson’s ratio. The application of SHAP also reveals that the relationship between the strut thickness and Poisson’s ratio is nonlinear. Finally, the minimum Poisson’s ratio value is achieved by design with minimum strut thickness, minimum node radius, and maximum strut length.

Czech name

—

Czech description

—

Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

—

OECD FORD branch

20501 - Materials engineering

Project

<a href="/en/project/EF16_019%2F0000766" target="_blank" >EF16_019/0000766: Engineering applications of microworld physics</a><br>

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2023

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Materials & Design

ISSN

0264-1275

e-ISSN

1873-4197

Volume of the periodical

232

Issue of the periodical within the volume

08

Country of publishing house

GB - UNITED KINGDOM

Number of pages

13

Pages from-to

1-13

UT code for WoS article

001147650600001

EID of the result in the Scopus database

2-s2.0-85164717707