Experimental investigation and ANFIS-Based modelling during machining of EN31 alloy steel

Ishwer Shivakoti, Lewlyn L.R. Rodrigues, Robert Cep, Premendra Mani Pradhan, Ashis Sharma, Akash Kumar Bhoi

Research output: Contribution to journalArticlepeer-review

6 Citations (Scopus)


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.

Original languageEnglish
Article number3137
Issue number14
Publication statusPublished - 07-2020

All Science Journal Classification (ASJC) codes

  • General Materials Science


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