Use of an Artificial Neural Network for Tensile Strength Prediction of Nano Titanium Dioxide Coated Cotton

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F46747885%3A24620%2F22%3A00009516" target="_blank" >RIV/46747885:24620/22:00009516 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.mdpi.com/2073-4360/14/5/937" target="_blank" >https://www.mdpi.com/2073-4360/14/5/937</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.3390/polym14050937" target="_blank" >10.3390/polym14050937</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Use of an Artificial Neural Network for Tensile Strength Prediction of Nano Titanium Dioxide Coated Cotton

Popis výsledku v původním jazyce

In this study, an artificial neural network (ANN) is used for the prediction of tensile strength of nano titanium dioxide (TiO2 ) coated cotton. The coating process was performed by ultraviolet (UV) radiations. Later on, a backpropagation ANN algorithm trained with Bayesian regularization was applied to predict the tensile strength. For a comparative study, ANN results were compared with traditional methods including multiple linear regression (MLR) and polynomial regression analysis (PRA). The input conditions for the experiment were dosage of TiO2, UV irradiation time and temperature of the system. Simulation results elucidated that ANN model provides high performance accuracy than MLR and PRA. In addition, statistical analysis was also performed to check the significance of this study. The results show a strong correlation between predicted and measured tensile strength of nano TiO2-coated cotton with small error values.

Název v anglickém jazyce

Use of an Artificial Neural Network for Tensile Strength Prediction of Nano Titanium Dioxide Coated Cotton

Popis výsledku anglicky

In this study, an artificial neural network (ANN) is used for the prediction of tensile strength of nano titanium dioxide (TiO2 ) coated cotton. The coating process was performed by ultraviolet (UV) radiations. Later on, a backpropagation ANN algorithm trained with Bayesian regularization was applied to predict the tensile strength. For a comparative study, ANN results were compared with traditional methods including multiple linear regression (MLR) and polynomial regression analysis (PRA). The input conditions for the experiment were dosage of TiO2, UV irradiation time and temperature of the system. Simulation results elucidated that ANN model provides high performance accuracy than MLR and PRA. In addition, statistical analysis was also performed to check the significance of this study. The results show a strong correlation between predicted and measured tensile strength of nano TiO2-coated cotton with small error values.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10404 - Polymer science

Projekt

<a href="/cs/project/EF16_025%2F0007293" target="_blank" >EF16_025/0007293: Modulární platforma pro autonomní podvozky specializovaných elektrovozidel pro dopravu nákladu a zařízení</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2022

Kód důvěrnosti údajů

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

Název periodika

Polymers

ISSN

2073-4360

e-ISSN

—

Svazek periodika

14

Číslo periodika v rámci svazku

5

Stát vydavatele periodika

CH - Švýcarská konfederace

Počet stran výsledku

11

Strana od-do

—

Kód UT WoS článku

000772931000001

EID výsledku v databázi Scopus

2-s2.0-85125592632