Machining performance of TiO2 embedded-glass fiber reinforced composites with snake optimizer

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F46747885%3A24210%2F24%3A00011994" target="_blank" >RIV/46747885:24210/24:00011994 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/46747885:24620/24:00011994

Výsledek na webu

<a href="https://www.sciencedirect.com/science/article/pii/S0263224124001374?via%3Dihub" target="_blank" >https://www.sciencedirect.com/science/article/pii/S0263224124001374?via%3Dihub</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.measurement.2024.114253" target="_blank" >10.1016/j.measurement.2024.114253</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Machining performance of TiO2 embedded-glass fiber reinforced composites with snake optimizer

Popis výsledku v původním jazyce

In this study, nano titanium dioxide-filled glass fiber reinforced polymer composites (nTiO2-GFRPC) are developed, and their surface roughness and machinability (cutting force) performance are evaluated with a newly evolved metaheuristic snake optimizer. A hybrid grey theory-snake optimizer (GT-SO) algorithm is developed where grey theory combines output responses (surface roughness and cutting force) into a single objective function, and the snake optimizer finds the optimal results. The novelty of this study is the compatibility of two different varieties of machine learning algorithms into one and the combination of two different responses (surface roughness and cutting force) into a single objective function. Process variables (nanoparticles amount, fiber volume fraction and feed rate), their interaction and their influence are designed by Taguchi orthogonal array and their optimization is performed by GT-SO. The optimal results are achieved with 5 % TiO2 (Weight %), 20 % fiber volume fraction and 75 mm/min feed rate. The optimum surface roughness and cutting force results were 1.49 μm and 1332.93 N, respectively. The validation of results shows that the output performance improved from 0.8929 to 0.9712, indicating the performance of the developed GT-SO with an 8.06 % error. The developed method was compared with other metaheuristics algorithms to reveal its potential for adaptation in composite material‘s cutting, milling, shaping and other machining characteristics. The results also confirm that TiO2 amount is a highly influencing factor for surface roughness calculations.

Název v anglickém jazyce

Machining performance of TiO2 embedded-glass fiber reinforced composites with snake optimizer

Popis výsledku anglicky

In this study, nano titanium dioxide-filled glass fiber reinforced polymer composites (nTiO2-GFRPC) are developed, and their surface roughness and machinability (cutting force) performance are evaluated with a newly evolved metaheuristic snake optimizer. A hybrid grey theory-snake optimizer (GT-SO) algorithm is developed where grey theory combines output responses (surface roughness and cutting force) into a single objective function, and the snake optimizer finds the optimal results. The novelty of this study is the compatibility of two different varieties of machine learning algorithms into one and the combination of two different responses (surface roughness and cutting force) into a single objective function. Process variables (nanoparticles amount, fiber volume fraction and feed rate), their interaction and their influence are designed by Taguchi orthogonal array and their optimization is performed by GT-SO. The optimal results are achieved with 5 % TiO2 (Weight %), 20 % fiber volume fraction and 75 mm/min feed rate. The optimum surface roughness and cutting force results were 1.49 μm and 1332.93 N, respectively. The validation of results shows that the output performance improved from 0.8929 to 0.9712, indicating the performance of the developed GT-SO with an 8.06 % error. The developed method was compared with other metaheuristics algorithms to reveal its potential for adaptation in composite material‘s cutting, milling, shaping and other machining characteristics. The results also confirm that TiO2 amount is a highly influencing factor for surface roughness calculations.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

21100 - Other engineering and technologies

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

2024

Kód důvěrnosti údajů

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

Název periodika

MEASUREMENT

ISSN

0263-2241

e-ISSN

—

Svazek periodika

227

Číslo periodika v rámci svazku

3

Stát vydavatele periodika

GB - Spojené království Velké Británie a Severního Irska

Počet stran výsledku

16

Strana od-do

1-16

Kód UT WoS článku

001182593400001

EID výsledku v databázi Scopus

2-s2.0-85184516764