Study of Surface Roughness and MRR in Turning of SiC Reinforced Al Alloy Composite Using Taguchi Design Method, ANN and PCA Approach under MQL Cutting Condition

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This paper emphases on the effect of various machining constraint on surface roughness and material removal rate in turning SiC reinforced Al alloy composite through taguchi orthogonal array based experimental analysis which has been further optimized using principal component analysis (PCA). Experimental investigation has been conducted under minimum quality lubricant (MQL) cutting environment. Palm oil has been used as lubricant where flow rate and pressure were kept at 120 ml/hr and 8 bar. The whole experiment has been designed using L25 orthogonal array having three input parameters and five different level to measure surface roughness and material removal rate. Taguchi S/N ratio-based optimization has been implemented where smaller the better criteria has been used for surface roughness whereas larger the better criteria has been used for material removal rate. From Analysis of variance, it is observed that cutting speed and feed rate are the most prominent factor for surface roughness. Nevertheless, Depth of cut and cutting speed are the most dominant factor for material removal rate. While comparing the predicted output values with experimental values, MAPE value is found in the range of 0.23 % for surface roughness and 0.045 % for material removal rate which is in very much tolerable range. Correlation coefficient value for experimental values of the resultant output is 0.98286 and 0.99869 respectively which signifies the effectiveness of the whole experiment. Subsequently, machining parameters were optimized using PCA technique. To attain satisfactory response values, depth of cut, cutting speed and feed rate need to be at 0.85 mm, 396 m/min and 0.16 mm/rev respectively. By applying the model, surface roughness of 0.7257 μm and MRR of 53856 mm3/min can be obtained. Keywords: SiC reinforced Al alloy; Turing; Minimum Quality Lubricant; Surface Roughness; MRR; Taguchi orthogonal array; Principal component analysis

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April 2020

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[1] 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.

DOI: 10.1016/j.jmrt.2015.05.003

Google Scholar

[2] Kumar R, Singh I, Kumar D. Electro discharge drilling of hybrid MMC. Procedia Eng [Internet]. 2013; 64: 1337–43. Available from: http://dx.doi.org/10.1016/j.proeng.2013.09.215.

DOI: 10.1016/j.proeng.2013.09.215

Google Scholar

[3] 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.

DOI: 10.1007/bf02717141

Google Scholar

[4] Alaneme KK, Bodunrin MO, Casting S. 6063 Metal Matrix Composites Developed By Two Step – Stir Casting Process. :105–10.

Google Scholar

[5] 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.

DOI: 10.1016/j.proeng.2014.11.821

Google Scholar

[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.

Google Scholar

[7] Ozben T, Kilickap E, Çakir O. Investigation of mechanical and machinability properties of SiC particle reinforced Al-MMC. J Mater Process Technol. 2008;198(1–3):220–5.

DOI: 10.1016/j.jmatprotec.2007.06.082

Google Scholar

[8] 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.

DOI: 10.1016/j.procir.2015.02.002

Google Scholar

[9] 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.

Google Scholar

[10] Dharmalingam S, Subramanian R, Somasundara Vinoth K, Anandavel B. Optimization of tribological properties in aluminum hybrid metal matrix composites using gray-taguchi method. J Mater Eng Perform. 2011;20(8):1457–66.

DOI: 10.1007/s11665-010-9800-4

Google Scholar

[11] Sukumar MS, Venkata Ramaiah P, Nagarjuna A. Optimization and prediction of parameters in face milling of Al-6061 using taguchi and ANN approach. Procedia Eng [Internet]. 2014;97:365–71. Available from: http://dx.doi.org/10.1016/j.proeng.2014.12.260.

DOI: 10.1016/j.proeng.2014.12.260

Google Scholar

[12] Tomadi SH, Ghani JA, Haron CHC, Ayu HM, Daud R. Effect of Cutting Parameters on Surface Roughness in End Milling of AlSi/AlN Metal Matrix Composite. Procedia Eng [Internet]. 2017;184:58–69. Available from: http://dx.doi.org/10.1016/j.proeng.2017.04.071.

DOI: 10.1016/j.proeng.2017.04.071

Google Scholar

[13] Pang JS, Ansari MNM, Zaroog OS, Ali MH, Sapuan SM. Taguchi design optimization of machining parameters on the CNC end milling process of halloysite nanotube with aluminium reinforced epoxy matrix (HNT/Al/Ep) hybrid composite. HBRC J [Internet]. 2014;10(2):138–44. Available from: http://linkinghub.elsevier.com/retrieve/pii/S1687404813000801.

DOI: 10.1016/j.hbrcj.2013.09.007

Google Scholar

[14] Shahrom MS, Yahya NM, Yusoff AR. Taguchi method approach on effect of lubrication condition on surface roughness in milling operation. Procedia Eng. 2013; 53: 594–9.

DOI: 10.1016/j.proeng.2013.02.076

Google Scholar

[15] Rao CJ, Rao DN, Srihari P. Influence of cutting parameters on cutting force and surface finish in turning operation. Procedia Eng [Internet]. 2013; 64: 1405–15. Available from: http://dx.doi.org/10.1016/j.proeng.2013.09.222.

DOI: 10.1016/j.proeng.2013.09.222

Google Scholar

[16] Saini SK, Pradhan SK. Optimization of multi-objective response during CNC turning using taguchi-fuzzy application. Procedia Eng [Internet]. 2014; 97: 141–9. Available from: http://dx.doi.org/10.1016/j.proeng.2014.12.235.

DOI: 10.1016/j.proeng.2014.12.235

Google Scholar

[17] Jayaraman P, Mahesh kumar L. Multi-response optimization of machining parameters of turning AA6063 T6 aluminium alloy using grey relational analysis in Taguchi method. Procedia Eng [Internet]. 2014; 97: 197–204. Available from: http://dx.doi.org/10.1016/j.proeng.2014.12.242.

DOI: 10.1016/j.proeng.2014.12.242

Google Scholar

[18] Moshat S, Datta S, Bandyopadhyay A, Pal P. Optimization of CNC end milling process parameters using PCA-based Taguchi method. Int J Eng Sci Technol [Internet]. 2010;2(1):95–102. Available from: http://www.ajol.info/index.php/ijest/article/view/59096.

DOI: 10.4314/ijest.v2i1.59096

Google Scholar

[19] Antony J. Multi-response optimization in industrial experiments using Taguchi's quality loss function and principal component analysis. Qual Reliab Eng Int. 2000;16(1):3–8.

DOI: 10.1002/(sici)1099-1638(200001/02)16:1<3::aid-qre276>3.0.co;2-w

Google Scholar

[20] Das MK, Kumar K, Barman TK, Sahoo P. Optimization of surface roughness and MRR in EDM using WPCA. Procedia Eng. 2013;64(November):446–55.

DOI: 10.1016/j.proeng.2013.09.118

Google Scholar

[21] Sahoo P, Pratap A, Bandyopadhyay A. Modeling and optimization of surface roughness and tool vibration in CNC turning of aluminum alloy using hybrid RSM-WPCA methodology. Int J Ind Eng Comput. 2017; 8(3):385–98.

DOI: 10.5267/j.ijiec.2016.11.003

Google Scholar