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

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

20501 - Materials engineering

Projekt

<a href="/cs/project/EF16_019%2F0000766" target="_blank" >EF16_019/0000766: Inženýrské aplikace fyziky mikrosvěta</a><br>

Návaznosti

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

Rok uplatnění

2023

Kód důvěrnosti údajů

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

Název periodika

Materials & Design

ISSN

0264-1275

e-ISSN

1873-4197

Svazek periodika

232

Číslo periodika v rámci svazku

08

Stát vydavatele periodika

GB - Spojené království Velké Británie a Severního Irska

Počet stran výsledku

13

Strana od-do

1-13

Kód UT WoS článku

001147650600001

EID výsledku v databázi Scopus

2-s2.0-85164717707