Experimental Investigation and ANFIS-Based Modelling During Machining of EN31 Alloy Steel

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27230%2F20%3A10245460" target="_blank" >RIV/61989100:27230/20:10245460 - isvavai.cz</a>

Výsledek na webu

<a href="http://apps.webofknowledge.com/full_record.do?product=WOS&search_mode=GeneralSearch&qid=2&SID=C1JANILcEYopFa1dX93&page=1&doc=3" target="_blank" >http://apps.webofknowledge.com/full_record.do?product=WOS&search_mode=GeneralSearch&qid=2&SID=C1JANILcEYopFa1dX93&page=1&doc=3</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.3390/ma13143137" target="_blank" >10.3390/ma13143137</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Experimental Investigation and ANFIS-Based Modelling During Machining of EN31 Alloy Steel

Popis výsledku v původním jazyce

This research presents the parametric effect of machining control variables while turning EN31 alloy steel with a Chemical Vapor deposited (CVD) Ti(C,N) + Al2O3+ TiN coated carbide tool insert. Three machining parameters with four levels considered in this research are feed, revolutions per minute (RPM), and depth of cut (a(p)). The influences of those three factors on material removal rate (MRR), surface roughness (Ra), and cutting force (Fc) were of specific interest in this research. The results showed that turning control variables has a substantial influence on the process responses. Furthermore, the paper demonstrates an adaptive neuro fuzzy inference system (ANFIS) model to predict the process response at various parametric combinations. It was observed that the ANFIS model used for prediction was accurate in predicting the process response at varying parametric combinations. The proposed model presents correlation coefficients of 0.99, 0.98, and 0.964 for MRR, Ra, and Fc, respectively.

Název v anglickém jazyce

Experimental Investigation and ANFIS-Based Modelling During Machining of EN31 Alloy Steel

Popis výsledku anglicky

This research presents the parametric effect of machining control variables while turning EN31 alloy steel with a Chemical Vapor deposited (CVD) Ti(C,N) + Al2O3+ TiN coated carbide tool insert. Three machining parameters with four levels considered in this research are feed, revolutions per minute (RPM), and depth of cut (a(p)). The influences of those three factors on material removal rate (MRR), surface roughness (Ra), and cutting force (Fc) were of specific interest in this research. The results showed that turning control variables has a substantial influence on the process responses. Furthermore, the paper demonstrates an adaptive neuro fuzzy inference system (ANFIS) model to predict the process response at various parametric combinations. It was observed that the ANFIS model used for prediction was accurate in predicting the process response at varying parametric combinations. The proposed model presents correlation coefficients of 0.99, 0.98, and 0.964 for MRR, Ra, and Fc, respectively.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

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OECD FORD obor

20300 - Mechanical engineering

Projekt

—

Návaznosti

V - Vyzkumna aktivita podporovana z jinych verejnych zdroju

Rok uplatnění

2020

Kód důvěrnosti údajů

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

Název periodika

Materials

ISSN

1996-1944

e-ISSN

—

Svazek periodika

13

Číslo periodika v rámci svazku

14

Stát vydavatele periodika

CH - Švýcarská konfederace

Počet stran výsledku

15

Strana od-do

—

Kód UT WoS článku

000554261600001

EID výsledku v databázi Scopus

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