TY - JOUR
T1 - Experimental investigation and ANFIS-Based modelling during machining of EN31 alloy steel
AU - Shivakoti, Ishwer
AU - Rodrigues, Lewlyn L.R.
AU - Cep, Robert
AU - Pradhan, Premendra Mani
AU - Sharma, Ashis
AU - Bhoi, Akash Kumar
N1 - Publisher Copyright:
© 2020 by the authors.
PY - 2020/7
Y1 - 2020/7
N2 - 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 (ap). 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.
AB - 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 (ap). 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.
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U2 - 10.3390/ma13143137
DO - 10.3390/ma13143137
M3 - Article
AN - SCOPUS:85088530734
SN - 1996-1944
VL - 13
JO - Materials
JF - Materials
IS - 14
M1 - 3137
ER -