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.

Department of Electrical and Electronics Engineering Sikkim Manipal Institute of Technology Sikkim Manipal University Majhitar 737136 India

Department of Humanities and Management Manipal Institute of Technology Manipal Academy of Higher Education Mangalore 576104 India

Department of Mechanical Engineering Sikkim Manipal Institute of Technology Sikkim Manipal University Majhitar 737136 India

VSB TU Ostrava Faculty of Mechanical Engineering 17 listopadu 2172 15 708 00 Ostrava Czech Republic

See more in PubMed

Shivakoti I., Kibria G., Pradhan P.M., Pradhan B.B., Sharma A. ANFIS based prediction and parametric analysis during turning operation of stainless steel 202. Mater. Manuf. Process. 2018;34:112–121. doi: 10.1080/10426914.2018.1512134. DOI

Jain H., Tripathi J., Bharilya R., Jain S., Kumar A. Optimisation and Evaluation of Machining Parameters for Turning Operation of Inconel-625. Mater. Today Proc. 2015;2:2306–2313. doi: 10.1016/j.matpr.2015.07.273. DOI

Mahamani A. Influence of process parameters on cutting force and surface roughness during turning of AA2219-TiB2/ZrB2 in-situ metal matrix composites. Procedia Mater. Sci. 2014;6:1178–1186. doi: 10.1016/j.mspro.2014.07.191. DOI

Jafarian F., Umbrello D., Golpayegani S., Darake Z. Experimental Investigation to Optimize Tool Life and Surface Roughness in Inconel 718 Machining. Mater. Manuf. Process. 2015;31:1–9. doi: 10.1080/10426914.2015.1090592. DOI

Alok A., Das M. Cost-effective way of hard turning with newly developed HSN2-coated tool. Mater. Manuf. Process. 2017;33:1–8. doi: 10.1080/10426914.2017.1388521. DOI

Amini S., Khakbaz H., Barani A. Improvement of Near-Dry Machining and Its Effect on Tool Wear in Turning of AISI 4142. Mater. Manuf. Process. 2014;30:241–247. doi: 10.1080/10426914.2014.952029. DOI

Baburaj E., Sundaram K.M.M., Senthil P. Effect of high speed turning operation on surface roughness of hybrid metal matrix (Al-SiCp-fly ash) composite. J. Mech. Sci. Technol. 2016;30:89–95. doi: 10.1007/s12206-015-1210-y. DOI

Suhail A.H., Ismail N., Wong S.V., Jalil N.A.A. Surface roughness identification using the grey relational analysis with multiple performance characteristics in turning operations. Arab. J. Sci. Eng. 2012;37:1111–1117. doi: 10.1007/s13369-012-0229-y. DOI

Sivaiah P., Chakradhar D. Analysis and modeling of cryogenic turning operation using response surface methodology. Silicon. 2018;10:2751–2768. doi: 10.1007/s12633-018-9816-1. DOI

Nadia J.N., Aaron F., Azuddin M. Influence of electromagnetic field on metal cutting in turning operation of AISI 1018 low carbon steel. IOP Conf. Ser.: Mater. Sci. Eng. 2017;210:12066. doi: 10.1088/1757-899X/210/1/012066. DOI

Khan A., Maity K. Application of MCDM-based TOPSIS method for the selection of optimal process parameter in turning of pure titanium. Benchmarking: Int. J. 2017;24:2009–2021. doi: 10.1108/BIJ-01-2016-0004. DOI

Asiltürk I., Neşeli S. Multi response optimization of CNC turning parameters via Taguchi method-based response surface analysis. Measurement. 2012;45:785–794. doi: 10.1016/j.measurement.2011.12.004. DOI

Jang J.S., Sun C.T. Neuro-fuzzy modelling andcontrol. Proc. IEEE. 1995;83:378–406. doi: 10.1109/5.364486. DOI

Alimam H., Hinnawi M., Pradhan P., Alkassar Y. ANN & ANFIS models for prediction of abrasive wear of 3105 aluminium alloy with polyurethane coating. Tribol. Ind. 2016;38:221–228.

Mia M., Krolczyk G.M., Maruda R.W., Wojciechowski S. Intelligent Optimization of Hard-Turning Parameters Using Evolutionary Algorithms for Smart Manufacturing. Materials. 2019;12:879. doi: 10.3390/ma12060879. PubMed DOI PMC

Xu L., Huang C., Li C., Wang J., Liu H., Wang X. Estimation of tool wear and optimization of cutting parameters based on novel ANFIS-PSO method toward intelligent machining. J. Intell. Manuf. 2020:1–14. doi: 10.1007/s10845-020-01559-0. DOI

Gill J., Singh J., Ohunakin O.S., Adelekan D.S., Atiba O.E., Nkiko M.O., Atayero A.A. Adaptive neuro-fuzzy inference system (ANFIS) approach for the irreversibility analysis of a domestic refrigerator system using LPG/TiO2 nanolubricant. Energy Rep. 2020;6:1405–1417. doi: 10.1016/j.egyr.2020.05.016. DOI

Noushabadi A.S., Dashti A., Raji M., Zarei A., Mohammadi A.H. Estimation of cetane numbers of biodiesel and diesel oils using regression and PSO-ANFIS models. Renew. Energy. 2020;158:465–473. doi: 10.1016/j.renene.2020.04.146. DOI

Jaypuria S., Mahapatra T.R., Jaypuria O. Metaheuristic tuned ANFIS model for input-output modeling of friction stir welding. Mater. Today Proc. 2019;18:3922–3930. doi: 10.1016/j.matpr.2019.07.332. DOI

Singh N.K., Singh Y., Kumar S., Sharma A. Predictive analysis of surface roughness in EDM using semi-empirical, ANN and ANFIS techniques: A comparative study. Mater. Today Proc. 2020;25:735–741. doi: 10.1016/j.matpr.2019.08.234. DOI

Yadav D., Chhabra D., Gupta R.K., Phogat A., Ahlawat A. Modeling and Analysis of Significant Process Parameters of FDM 3D Printer using ANFIS. Volume 21. Elsevier BV; Amsterdam, The Netherlands: 2020. pp. 1592–1604.

Jalal M., Grasley Z., Nassir N., Jalal H. Strength and dynamic elasticity modulus of rubberized concrete designed with ANFIS modeling and ultrasonic technique. Constr. Build. Mater. 2020;240:117920. doi: 10.1016/j.conbuildmat.2019.117920. DOI

Shehabeldeen T.A., Zhou J., Shen X., Yin Y., Ji X. Comparison of RSM with ANFIS in predicting tensile strength of dissimilar friction stir welded AA2024-AA5083 aluminium alloys. Procedia Manuf. 2019;37:555–562. doi: 10.1016/j.promfg.2019.12.088. DOI

Mostafaei M. ANFIS models for prediction of biodiesel fuels cetane number using desirability function. Fuel. 2018;216:665–672. doi: 10.1016/j.fuel.2017.12.025. DOI

Sadeghizadeh A., Ebrahimi F., Heydari M., Tahmasebikohyani M., Ebrahimi F., Sadeghizadeh A. Adsorptive removal of Pb (II) by means of hydroxyapatite/chitosan nanocomposite hybrid nanoadsorbent: ANFIS modeling and experimental study. J. Environ. Manag. 2019;232:342–353. doi: 10.1016/j.jenvman.2018.11.047. PubMed DOI

Zhou J., Li C., Arslan C.A., Hasanipanah M., Amnieh H.B. Performance evaluation of hybrid FFA-ANFIS and GA-ANFIS models to predict particle size distribution of a muck-pile after blasting. Eng. Comput. 2019:1–10. doi: 10.1007/s00366-019-00822-0. DOI

Gill J., Singh J. An applicability of ANFIS approach for depicting energetic performance of VCRS using mixture of R134a and LPG as refrigerantApplicabilité de l’approche ANFIS pour décrire la performance énergétique d’un systèmefrigorifique à compression de vapeurfonctionnant avec un mélange de R134a et de GPL commefrigorigène. Int. J. Refrig. 2018;85:353–375.

Yaseen Z.M., Ebtehaj I., Bonakdari H., Deo R.C., Mehr A.D., Mohtar W.H.M.W., Diop L., El-Shafie A., Singh V.P. Novel approach for streamflow forecasting using a hybrid ANFIS-FFA model. J. Hydrol. 2017;554:263–276. doi: 10.1016/j.jhydrol.2017.09.007. DOI

Soroush E., Mesbah M., Hajilary N., Rezakazemi M. ANFIS modeling for prediction of CO2 solubility in potassium and sodium based amino acid Salt solutions. J. Environ. Chem. Eng. 2019;7:102925. doi: 10.1016/j.jece.2019.102925. DOI

Kar S., Pandit A.R., Biswal K.C. Prediction of FRP shear contribution for wrapped shear deficient RC beams using adaptive neuro-fuzzy inference system (ANFIS) Structures. 2020;23:702–717. doi: 10.1016/j.istruc.2019.10.022. DOI

Wojciechowski S., Maruda R., Barrans S.M., Nieslony P., Krolczyk G.M. Optimisation of machining parameters during ball end milling of hardened steel with various surface inclinations. Measurement. 2017;111:18–28. doi: 10.1016/j.measurement.2017.07.020. DOI

Mia M., Gupta M.K., Lozano J.A., Carou D., Pimenov D.Y., Krolczyk G.M., Khan A.M., Dhar N.R. Multi-objective optimization and life cycle assessment of eco-friendly cryogenic N2 assisted turning of Ti-6Al-4V. J. Clean. Prod. 2019;210:121–133. doi: 10.1016/j.jclepro.2018.10.334. DOI

Informa UK Limited . Metalworking Fluids. 3rd ed. Informa UK Limited; London, UK: 2017.