Predictive Modeling of Surface Roughness in MQL assisted Turning of SiC-Al Alloy Composites using Artificial Neural Network and Adaptive Neuro Fuzzy Inference System
This research work deals with the analysis of surface roughness in turning SiC-Al Alloy composite through experimental investigation and predictive model formulation using Artificial neural network (ANN) and Adaptive neuro fuzzy inference system (ANFIS). The experiment has been carried out in MQL cutting condition where cutting speed, feed rate and depth of cut have been considered as input parameters to check the desired surface roughness response. Artificial neural network model with four different learning algorithms was used to predict surface roughness. Best performance was attained by 3-6-1 network structure with LM learning algorithm for training dataset and SCG learning algorithm for testing dataset. Higher correlation coefficient value of 0.99932 and 0.99888 prove the adequacy of the predicted ANN model. However, MAPE for ANN predicted value is 5.298 %. ANFIS model has been designed utilizing Gaussian membership function (Gaussmf) with 3 membership function for every input parameter and linear membership function for output parameter. For training both back propagation and hybrid method, 3 membership function were used. Based on the comparison of ANFIS with three types of membership function parameter training, hybrid method provides accurate results as it portrays MAPE value of 0.113542 when compared to the higher MAPE value of back propagation method. Surface viewer plot also suggested the effectiveness of hybrid model as it generates lower value of surface roughness. On the basis of mean absolute percentage error, it can be further concluded that hybrid method can make an impartially accurate prediction of surface roughness in comparison to back propagation method and artificial neural network which does prove the expediency of proposed hybrid model to reduce the surface roughness considerably.
Bodunrin MO, Alaneme KK, Chown LH. Aluminium matrix hybrid composites: A review of reinforcement philosophies; Mechanical, corrosion and tribological characteristics. J Mater Res Technol [Internet]. 2015;4(4):434–45. Available from: http://dx.doi. org/10.1016/j.jmrt.2015.05.003
Surappa MK. Aluminium matrix composites: Challenges and opportunities. Sadhana [Internet]. 2003;28(1–2):319–34. Available from: http://link. springer.com/10.1007/BF02717141
Alaneme KK, Bodunrin MO, Casting S. 6063 Metal Matrix Composites Developed By Two Step – Stir Casting Process. :105–10.
Rahman MH, Al Rashed HMM. Characterization of silicon carbide reinforced aluminum matrix Composites. Procedia Eng [Internet]. 2014;90:103–9. Available from: http://dx.doi.org/10.1016/j.proeng.2014.11.821
Kant G, Sangwan KS. Predictive modelling and optimization of machining parameters to minimize surface roughness using artificial neural network coupled with genetic algorithm. Procedia CIRP [Internet]. 2015;31:453–8. Available from: http:// dx.doi.org/10.1016/j.procir.2015.03.043
[Internet]. 2015;31:453–8. Available from: http:// dx.doi.org/10.1016/j.procir.2015.03.043 reinforced AlMMC. J Mater Process Technol. 2008;198(1–3):220–5.
Kılıçkap E, Cakir O, Aksoy M, İnan A. Study of tool wear and surface roughness in machining of homogenised SiC-p reinforced aluminium metal matrix composite. Vol. 164, Journal of Materials Processing Technology. 2005. 862-867 p.
Chandrasekaran M, Devarasiddappa D. Development of Predictive Model for Surface Roughness in end Milling of Al-SiCp Metal Matrix Composites using Fuzzy Logic. 2012;109(7):1271–6.
Pandiyan V, Caesarendra W, Tjahjowidodo T, Praveen G. Predictive Modelling and Analysis of Process Parameters on Material Removal Characteristics in Abrasive Belt Grinding Process. Appl Sci [Internet]. 2017;7(4):363. Available from: http://www.mdpi. com/2076-3417/7/4/363
Reddy BS, Kumar JS, Reddy KVK. Prediction of Surface Roughness in Turning Using Adaptive Neuro-Fuzzy Inference System. 2009;3(4):252–9.
Kanish TC, Kuppan P, Narayanan S, Denis Ashok S. A fuzzy logic based model to predict the improvement in surface roughness in magnetic field assisted abrasive finishing. Procedia Eng [Internet]. 2014;97:1948– 56. Available from: http://dx.doi.org/10.1016/j. proeng.2014.12.349
Abhishek K, Panda BN, Datta S, Mahapatra SS. Comparing Predictability of Genetic Programming and ANFIS on Drilling Performance Modeling for GFRP Composites. Procedia Mater Sci [Internet]. 2014;6(Icmpc):544–50. Available from: http://linkinghub.elsevier.com/ retrieve/pii/S2211812814004349
Udhayakumar G. Experimental Investigation of Stainless Steel 316L Micro Electro Chemical Process using RSM and. 2017;3(38):57–62.
Teimouri R, Baseri H, Moharami R. Multi-responses optimization of ultrasonic machining process. J Intell Manuf [Internet]. 2015;26(4):745–53. Available from: http://link.springer.com/10.1007/s10845-013-0831-1 15. Karim R, Ahmed S, Salahuddin S, Karim R. Optimization of Machining Parameter for Surface Roughness in Turning GFRP Composite Using RSM-GA Approach. 2018;6(x).
Altinkok N. Use of Artificial Neural Network for Prediction of Mechanical Properties of α-Al 2 O 3 Particulatereinforced Al–Si10Mg Alloy Composites Prepared by using Stir Casting Process. J Compos Mater [Internet]. 2006;40(9):779–96. Available from: http://journals. sagepub.com/doi/10.1177/0021998305055547
Sangwan KS, Saxena S, Kant G. Optimization of machining parameters to minimize surface roughness using integrated ANN-GA approach. Procedia CIRP [Internet]. 2015;29:305–10. Available from: http:// dx.doi.org/10.1016/j.procir.2015.02.002
Prof KG, Prajapati A V, Mechanical P, College ELD, Cam MECAD, College LD, et al. Vibration Analysis of Lathe Machine Engineering Nemisha Goswami Student of M . E . ( CAD / CAM ) L . D . College of Engineering Mechanical. 2013;(2277):88–90
Patwari MAU, Amin AKMN, Faris W, Sharulhazrin MS, Hafizzudin I. A new technique for the investigation of chatter formation during end milling of medium carbon steel (AISI 45). Proc Int MultiConference Eng Comput Sci 2010, IMECS 2010. 2010;III(Aisi 45):1812–6.
Hossain SJ, Ahmad N. Artificial Intelligence Based Surface Roughness Prediction Modeling for Three Dimensional End Milling. Int J Adv Sci Technol. 2012;45:1–18.
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