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Open AccessArticle

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

1
Department of Mechanical Engineering, Sikkim Manipal Institute of Technology, Sikkim Manipal University, Majhitar 737136, India
2
Department of Humanities and Management, Manipal Institute of Technology, Manipal Academy of Higher Education, Mangalore 576104, India
3
VSB-TU Ostrava, Faculty of Mechanical Engineering, 17. listopadu 2172/15, 708 00 Ostrava, Czech Republic
4
Department of Electrical and Electronics Engineering, Sikkim Manipal Institute of Technology, Sikkim Manipal University, Majhitar 737136, India
*
Author to whom correspondence should be addressed.
Materials 2020, 13(14), 3137; https://doi.org/10.3390/ma13143137
Received: 14 May 2020 / Revised: 9 July 2020 / Accepted: 10 July 2020 / Published: 14 July 2020
(This article belongs to the Special Issue Machining and Manufacturing of Alloys and Steels)
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. View Full-Text
Keywords: alloy steel; feed; ANFIS; RPM; turning alloy steel; feed; ANFIS; RPM; turning
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MDPI and ACS Style

Shivakoti, I.; Rodrigues, L.L.R.; Cep, R.; Pradhan, P.M.; Sharma, A.; Kumar Bhoi, A. Experimental Investigation and ANFIS-Based Modelling During Machining of EN31 Alloy Steel. Materials 2020, 13, 3137.

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