Abstract
In this approach, response surface methodology (RSM) and artificial neural network (ANN) techniques were used in order to search for optimal prediction of uncontrollable machining factors that leads to better machining performance. The experiment has been established using 3 levels and 4 factors Box-Behnken design (BBD) for tangential force and surface roughness measurements according to combinations of cutting speed, feed rate, and cutting depth using multilayer-coated tungsten carbide insert with various nose radius in turning of X210Cr12 steel under dry, wet, and MQL machining. Consequently, it could be possible to investigate the efficiency of MQL technique for an environment-friendly ecological machining. Then, a comparative between ANN and RSM models has been established to determine the best approach according to model accuracy and capability for predicting surface roughness and cutting force. The ANN method provides more accurate results and proved its effectiveness as soon as its correlation coefficients, mean prediction errors (MPEs), and root mean square errors are rather small compared to those obtained by the RSM method.
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Weinert K, Inasaki I, Sutherland JW, Wakabayashi T (2004) Dry machining and minimum quantity lubrication. CIRP Ann Manuf Technol 53(2):511–537
Sharma AK, Tiwari AK, Dixit AR (2016) Effects of minimum quantity lubrication (MQL) in machining processes using conventional and nanofluid based cutting fluids: a comprehensive review. J Clean Prod 127:1–18
Cozzens DA, Rao PD, Olson WW, Sutherland JW, Panetta JM (1999) An experimental investigation into the effect of cutting fluid conditions on the boring of aluminum alloys. J Manuf Sci Eng 121(3):434–439
Khan MMA, Mithu MAH, Dhar NR (2009) Effects of minimum quantity lubrication on turning AISI 9310 alloy steel using vegetable oil-based cutting fluid. J Mater Process Technol 209(15):5573–5583
Derflinger V, Brändle H, Zimmermann H (1999) New hard/lubricant coating for dry machining. Surf Coat Technol 113(3):286–292
Soković M, Mijanović K (2001) Ecological aspects of the cutting fluids and its influence on quantifiable parameters of the cutting processes. J Mater Process Technol 109(1):181–189
Tan XC, Liu F, Cao HJ, Zhang H (2002) A decision-making framework model of cutting fluid selection for green manufacturing and a case study. J Mater Process Technol 129(1):467–470
Rahim EA, Ibrahim MR, Rahim AA, Aziz S, Mohid Z (2015) Experimental investigation of minimum quantity lubrication (MQL) as a sustainable cooling technique. Procedia CIRP 26:351–354
Dhar NR, Kamruzzaman M, Ahmed M (2006) Effect of minimum quantity lubrication (MQL) on tool wear and surface roughness in turning AISI-4340 steel. J Mater Process Technol 172(2):299–304
Varadarajan AS, Philip PK, Ramamoorthy B (2002) Investigations on hard turning with minimal cutting fluid application (HTMF) and its comparison with dry and wet turning. Int J Mach Tools Manuf 42(2):193–200
Hadad M, Sadeghi B (2013) Minimum quantity lubrication-MQL turning of AISI 4140 steel alloy. J Clean Prod 54:332–343
Dixit US, Sarma DK, Davim JP (2012) Environmentally friendly machining. Springer Science & Business Media, Berlin
Asiltürk I, Neşeli S (2012) Multi response optimisation of CNC turning parameters via Taguchi method-based response surface analysis. Measurement 45(4):785–794
Elbah M, Aouici H, Meddour I, Yallese MA, Boulanouar L (2016) Application of response surface methodology in describing the performance of mixed ceramic tool when turning AISI 4140 steel. Mech Ind 17(3):309
Kasim MS, Sulaiman MA (2013) Prediction surface roughness in high-speed milling of Inconel 718 under MQL using RSM method. Middle-East J Sci Res 13(3):264–272
Chabbi A, Yallese MA, Meddour I, Nouioua M, Mabrouki T, Girardin F (2017) Predictive modeling and multi-response optimization of technological parameters in turning of Polyoxymethylene polymer (POM C) using RSM and desirability function. Measurement 95:99–115
Das B, Roy S, Rai RN, Saha SC (2015) Studies on effect of cutting parameters on surface roughness of al-cu-TiC MMCs: an artificial neural network approach. Procedia Comput Sci 45:745–752
Palavar O, Özyürek D, Kalyon A (2015) Artificial neural network prediction of aging effects on the wear behavior of IN706 superalloy. Mater des 82:164–172
Kant G, Sangwan KS (2015) Predictive modelling and optimization of machining parameters to minimize surface roughness using artificial neural network coupled with genetic algorithm. Procedia CIRP 31:453–458
Rao KV, Murthy PBGSN (2016) Modeling and optimization of tool vibration and surface roughness in boring of steel using RSM, ANN and SVM. J Intell Manuf:1–11
Ranganathan S, Senthilvelan T, Sriram G (2010) Evaluation of machining parameters of hot turning of stainless steel (type 316) by applying ANN and RSM. Mater Manuf Process 25(10):1131–1141
Bingöl D, Hercan M, Elevli S, Kılıç E (2012) Comparison of the results of response surface methodology and artificial neural network for the biosorption of lead using black cumin. Bioresour Technol 112:111–115
Lakshminarayanan AK, Balasubramanian V (2009) Comparison of RSM with ANN in predicting tensile strength of friction stir welded AA7039 aluminium alloy joints. Trans Nonferrous Metals Soc China 19(1):9–18
Kumar R, Chauhan S (2015) Study on surface roughness measurement for turning of al 7075/10/SiCp and Al 7075 hybrid composites by using response surface methodology (RSM) and artificial neural networking (ANN). Measurement 65:166–180
Ezugwu EO, Fadare DA, Bonney J, Da Silva RB, Sales WF (2005) Modelling the correlation between cutting and process parameters in high-speed machining of Inconel 718 alloy using an artificial neural network. Int J Mach Tools Manuf 45(12):1375–1385
Meddour I, Yallese MA, Khattabi R, Elbah M, Boulanouar L (2015) Investigation and modeling of cutting forces and surface roughness when hard turning of AISI 52100 steel with mixed ceramic tool: cutting conditions optimization. Int J Adv Manuf Technol 77(5–8):1387–1399
Yücel E, Günay M (2013) Modelling and optimization of the cutting conditions in hard turning of high-alloy white cast iron (Ni-hard). Proc Inst Mech Eng C J Mech Eng Sci 227(10):2280–2290
Hwang YK, Lee CM (2010) Surface roughness and cutting force prediction in MQL and wet turning process of AISI 1045 using design of experiments. J Mech Sci Technol 24(8):1669–1677
Bouzid L, Yallese MA, Chaoui K, Mabrouki T, Boulanouar L (2015) Mathematical modeling for turning on AISI 420 stainless steel using surface response methodology. Proc Inst Mech Eng B J Eng Manuf 229(1):45–61
Fausett L (1994) Fundamentals of neural networks: architectures, algorithms, and applications. Prentice-Hall, Inc., New Jersey
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Nouioua, M., Yallese, M.A., Khettabi, R. et al. Investigation of the performance of the MQL, dry, and wet turning by response surface methodology (RSM) and artificial neural network (ANN). Int J Adv Manuf Technol 93, 2485–2504 (2017). https://doi.org/10.1007/s00170-017-0589-2
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DOI: https://doi.org/10.1007/s00170-017-0589-2