Multi-response optimization for sequential application of erosion–abrasion in face grinding configuration

References

• B. Mohan, A. Rajadurai, and K. G. Satyanarayana, “Electric discharge machining of Al-SiC metal matrix composites using rotary tube electrode,” J. Mater. Process. Technol., vol. 153–154, pp. 978–985, 2004. DOI: 10.1016/j.jmatprotec.2004.04.347.
   Web of Science ®Google Scholar
• A. R. Ahamed, P. Asokan, and S. Aravindan, “EDM of hybrid Al-SiCp-B4Cp and Al-SiCp-glass MMCs,” Int. J. Adv. Manuf. Technol., vol. 44, no. 5–6, pp. 520–528, 2009. DOI: 10.1007/s00170-008-1839-0.
   Web of Science ®Google Scholar
• G. Qian, Y. Feng, B. Li, S. Huang, H. Liu, and K. Ding, “Effect of electrical current on the tribological behavior of the Cu-WS2-G composites in air and vacuum,” Chin. J. Mech. Eng., vol. 26, no. 2, pp. 384–392, 2013. DOI: 10.3901/CJME.2013.02.384.
   Web of Science ®Google Scholar
• Z. W. Zhong, “Grinding of aluminium-based metal matrix composites reinforced with Al2O3 or SiC particles,” Int. J. Adv. Manuf. Technol., vol. 21, no. 2, pp. 79–83, 2003. DOI: 10.1007/s001700300009.
   Web of Science ®Google Scholar
• E. R. I. Mahmoud, M. Takahashi, T. Shibayanagi, and K. Ikeuchi, “Wear characteristics of surface-hybrid-MMCs layer fabricated on aluminum plate by friction stir processing,” Wear, vol. 268, no. 9–10, pp. 1111–1121, 2010. DOI: 10.1016/j.wear.2010.01.005.
   Web of Science ®Google Scholar
• S. T. Huang, L. Zhou, J. Chen, and L. F. Xu, “Drilling of SiCp/Al metal matrix composites with polycrystalline diamond (PCD) tools, Mater,” Manuf. Process., vol. 27, no. 10, pp. 1090–1094, 2012. DOI: 10.1080/10426914.2011.654152.
   Web of Science ®Google Scholar
• J. P. Davim, “Diamond tool performance in machining metal-matrix composites,” J. Mater. Process. Technol., vol. 128, no. 1–3, pp. 100–105, 2002. DOI: 10.1016/S0924-0136(02)00431-4.
   Web of Science ®Google Scholar
• J. Kopac and P. Krajnik, “High performance grinding – a review,” J. Mater. Process. Technol., vol. 175, no. 1–3, pp. 278–284, 2006. DOI: 10.1016/j.jmatprotec.2005.04.010.
   Web of Science ®Google Scholar
• Z. Zhong and N. P. Hung, “Grinding of alumina/aluminum composites,” J. Mater. Process. Technol., vol. 123, no. 1, pp. 13–17, 2002. DOI: 10.1016/S0924-0136(02)00075-4.
   Web of Science ®Google Scholar
• J. B. J. W. Hegeman, J. T. M. G. Hosson, and G. With, “Grinding of WC-Co hard metals,” Wear, vol. 248, no. 1–2, pp. 187–196, 2001. DOI: 10.1016/S0043-1648(00)00561-5.
   Google Scholar
• J. D. Kim, Y. H. Kang, D. X. Jin, and Y. S. Lee, “Development of discontinuous grinding wheel with multi porous grooves,” Int. J. Mach. Tools Manuf., vol. 37, no. 11, pp. 1611–1624, 1997. DOI: 10.1016/S0890-6955(97)00005-9.
   Web of Science ®Google Scholar
• F. Muller and J. Monaghan, “Non-conventional machining of particle reinforced metal matrix composite,” Int. J. Mach. Tools Manuf., vol. 40, no. 9, pp. 1351–1366, 2000. DOI: 10.1016/S0890-6955(99)00121-2.
   Web of Science ®Google Scholar
• B. Mohan, A. Rajadurai, and K. G. Satyanarayana, “Effect of SiC and rotation of electrode on electric discharge machining of Al/SiC composite,” J. Mater. Process. Technol., vol. 124, no. 3, pp. 297–304, 2002. DOI: 10.1016/S0924-0136(02)00202-9.
   Web of Science ®Google Scholar
• B. C. Abothula, V. Yadava, and G. K. Singh, “Development and experimental study of electrodischarge face grinding,” Mater. Manuf. Process., vol. 25, no. 6, pp. 482–487, 2010. DOI: 10.1080/10426910903367436.
   Web of Science ®Google Scholar
• R. N. Yadav and V. Yadava, “A new way of electro-abrasion hybrid machining (EAHM) using slotted-diamond grinding wheel,” Int. J. Manuf. Technol. Manag., vol. 28, no. 1/2/3, pp. 132–145, 2014. DOI: 10.1504/IJMTM.2014.064625.
   Google Scholar
• A. Ghosh and A. K. Mallik, Manufacturing Science. New Delhi: East-West Press, 1999.
   Google Scholar
• P. Koshy, V. K. Jain, and G. K. Lal, “Mechanism of material removal in electrical discharge diamond grinding,” Int. J. Mach. Tools Manuf., vol. 36, no. 10, pp. 1173–1185, 1996. DOI: 10.1016/0890-6955(95)00103-4.
   Web of Science ®Google Scholar
• S. K. Choudhury, V. K. Jain, and M. Gupta, “Electrical discharge diamond grinding of high speed steel,” Mach. Sci. Technol., vol. 3, no. 1, pp. 91–105, 1999. DOI: 10.1080/10940349908945685.
   Web of Science ®Google Scholar
• S. K. S. Yadav, V. Yadava, and V. L. Narayana, “Experimental study and parameter design of electro-discharge diamond grinding,” Int. J. Adv. Manuf. Technol., vol. 36, no. 1–2, pp. 34–42, 2008. DOI: 10.1007/s00170-006-0820-z.
   Web of Science ®Google Scholar
• G. K. Singh, V. Yadava, and R. Kumar, “Multiresponse optimization of electro-discharge diamond face grinding process using robust design of experiments,” Mater. Manuf. Process., vol. 25, no. 8, pp. 851–856, 2010. DOI: 10.1080/10426910903536758.
   Web of Science ®Google Scholar
• R. Ji, Y. Liu, Y. Zhang, F. Wang, B. Cai, and X. Dong, “Machining performance optimization in end ED milling and mechanical grinding compound process,” Mater. Manuf. Process., vol. 27, no. 2, pp. 221–228, 2012. DOI: 10.1080/10426914.2011.568569.
   Web of Science ®Google Scholar
• R. N. Yadav and V. Yadava, “Influence of input parameters on machining performances of slotted-electrical discharge abrasive grinding of Al/SiC/Gr metal matrix composite,” Mater. Manuf. Process., vol. 28, no. 12, pp. 1361–1369, 2013. DOI: 10.1080/10426914.2013.832300.
   Web of Science ®Google Scholar
• R. N. Yadav and V. Yadava, “Slotted-electrical discharge diamond cut-off grinding of Al/SiC/B4C hybrid metal matrix composite,” J. Mech. Sci. Technol., vol. 28, no. 1, pp. 309–316, 2014. DOI: 10.1007/s12206-013-0968-z.
   Web of Science ®Google Scholar
• R. N. Yadav and V. Yadava, “Machining of a hybrid–metal matrix composite using an erosion–abrasion-based compound wheel in electrical discharge grinding,” Particul. Sci. Technol., vol. 35, no. 4, pp. 494–504, 2017. DOI: 10.1080/02726351.2016.1170743.
   Web of Science ®Google Scholar
• J. Kozak, “Abrasive electrodischarge grinding (AEDG) of advanced materials,” Arch. Civ. Mech. Eng., vol. 2, pp. 83–101, 2002.
   Google Scholar
• S. Kumar and S. K. Choudhury, “Prediction of wear and surface roughness in electro-discharge diamond grinding,” J. Mater. Process. Technol., vol. 191, no. 1–3, pp. 206–209, 2007. DOI: 10.1016/j.jmatprotec.2007.03.032.
   Web of Science ®Google Scholar
• S. K. S. Yadav and V. Yadava, “Neural network modeling and multi objective optimization of electrical discharge diamond cut-off grinding (EDDCG),” Int. J. Abras. Technol., vol. 4, no. 4, pp. 346–362, 2011. DOI: 10.1504/IJAT.2011.044509.
   Google Scholar
• S. S. Agrawal and V. Yadava, “Modeling and prediction of material removal rate and surface roughness in surface-electrical discharge diamond grinding process of metal matrix composites,” Mater. Manuf. Process., vol. 28, no. 4, pp. 381–389, 2013. DOI: 10.1080/10426914.2013.763678.
   Web of Science ®Google Scholar
• R. N. Yadav, V. Yadava, and G. K. Singh, “Modeling and simulation of spark assisted diamond face grinding of tungsten carbide-cobalt composite,” Int. J. Manuf. Technol. Manag., vol. 28, no. 1/2/3, pp. 146–163, 2014. DOI: 10.1504/IJMTM.2014.064624.
   Google Scholar
• M. R. Sunny and R. K. Kapania, “Artificial neural network based identification of a modified dynamic preisach model,” Int. J. Comput. Methods Eng. Sci. Mech., vol. 15, no. 1, pp. 45–53, 2014. DOI: 10.1080/15502287.2013.834001.
   Google Scholar
• I. Lakshmanan and S. Ramasam, “An artificial neural-network approach to software reliability growth modeling,” Proc. Comput. Sci, vol. 57, pp. 695–702, 2015. DOI: 10.1016/j.procs.2015.07.450.
   Google Scholar
• W. Alaloul, M. S. Liew, N. A. W. Zawawi, B. S. Mohammed, M. Adamu, and H. A. Azia, “An artificial neural networks (ANN) model for evaluating construction project performance based on coordination factors,” Cogent Eng., vol. 5, pp. 1–18, 2018.
   Web of Science ®Google Scholar
• D. K. Kasdekar, V. Parashar, and C. Arya, “Artificial neural network models for the prediction of MRR in electro-chemical machining,” Mater Today: Proc., vol. 5, no. 1, pp. 772–779, 2018. DOI: 10.1016/j.matpr.2017.11.146.
   Google Scholar
• P. J. Zucatelli, E. G. S. Nascimento, A. Á. B. Santos, and D. M. Moreira, “Nowcasting prediction of wind speed using computational intelligence and wavelet in Brazil,” Int. J. Comput. Methods Eng. Sci. Mech., vol. 21, pp. 243–269, 2020.
   Web of Science ®Google Scholar
• C. H. Manoj and M. Biglarbegian, “An assessment of shallow neural network for stress updates in computational solid mechanics,” Int. J. Comput. Methods Eng. Sci. Mech., vol. 21, no. 6, pp. 277–291, 2020. DOI: 10.1080/15502287.2020.1836534.
   Web of Science ®Google Scholar
• E. Y. Ng, N. Liu, and J. H. Siak, “Numerical parametric study of inlet distortion propagation in compressor using integral approach with Taguchi method,” Int. J. Comput. Methods Eng. Sci. Mech, vol. 6, pp. 167–177, 2005.
   Google Scholar
• E. M. Anawa and A. G. Olabi, “Using Taguchi method to optimize welding pool of dissimilar laser-welded components,” Opt. Laser Technol., vol. 40, no. 2, pp. 379–388, 2008. DOI: 10.1016/j.optlastec.2007.07.001.
   Web of Science ®Google Scholar
• G. K. Singh, V. Yadava, and R. Kumar, “Robust parameter design and multi-objective optimisation of electro-discharge diamond face grinding of HSS,” Int. J. Mech. Mech. Mater., vol. 11, no. 1, pp. 1–19, 2012. DOI: 10.1504/IJMMM.2012.044919.
   Google Scholar
• K. Palanikumar, “EDM process parameters optimization using Taguchi method,” Appl. Mech. Mater., vol. 766–767, pp. 867–872, 2015.
   Google Scholar
• S. Chandramouli and K. Eswaraiah, “Experimental investigation of EDM process parameters in machining of 17-4 PH steel using Taguchi method,” Mater. Today: Proc., vol. 5, no. 2, pp. 5058–5067, 2018. DOI: 10.1016/j.matpr.2017.12.084.
   Google Scholar
• K. Velayutham, K. Venkadeshwaran, and G. Selvakumar, “Optimisation of laser cutting of SS 430 plate using advanced Taguchi entropy weighted-based GRA methodology,” Int J. Mech. Manuf. Syst., vol. 11, no. 2/3, pp. 148–166, 2018. DOI: 10.1504/IJMMS.2018.10013933.
   Google Scholar
• S. Sahu, J. Ali, P. Yupapin, and G. Singh, “Effectiveness of Taguchi method for the optimization of narrowband optical filters based on grating waveguides,” Microsyst. Technol., vol. 25, no. 3, pp. 789–795, 2019. DOI: 10.1007/s00542-018-4026-8.
   Web of Science ®Google Scholar
• S. U. Ahmed and R. Arora, “Quality characteristics optimization in CNC end milling of A36 K02600 using Taguchi’s approach coupled with artificial neural network and genetic algorithm,” Int. J. Syst. Assur. Eng. Manag., vol. 10, no. 4, pp. 676–695, 2019. DOI: 10.1007/s13198-019-00796-8.
   Web of Science ®Google Scholar
• M. Aamir, S. Tu, M. T. Rad, K. Giasin, and A. Vafadar, “Optimization and modeling of process parameters in multi-hole simultaneous drilling using Taguchi method and Fuzzy Logic approach,” Materials, vol. 13, no. 3, pp. 680–696, 2020. DOI: 10.3390/ma13030680.
   Web of Science ®Google Scholar
• S. Kumar, R. N. Yadav, and R. Kumar, “Empirical modeling and multi-response optimization of duplex turning for Ni-718 alloy,” Int. J. Syst. Assur. Eng. Manag., vol. 11, no. 1, pp. 126–139, 2020. DOI: 10.1007/s13198-019-00931-5.
   Web of Science ®Google Scholar
• S. Kumar, R. N. Yadav, and R. Kumar, “Experimental studies and multi-response optimisation of duplex turning parameters using grey relational analysis with entropy measurement,” Int. J. Precis. Technol., vol. 9, no. 1, pp. 71–90, 2020. DOI: 10.1504/IJPTECH.2020.109777.
   Google Scholar
• D. Mandal, S. K. Pal, and P. Saha, “Modeling of electrical discharge machining process using back propagation neural network and multi-objective optimization using non-dominating sorting genetic algorithm-II,” J. Mater. Process. Technol., vol. 186, no. 1–3, pp. 154–162, 2007. DOI: 10.1016/j.jmatprotec.2006.12.030.
   Web of Science ®Google Scholar
• N. K. Jain, V. K. Jain, and K. Deb, “Optimization of process parameters of mechanical type advanced machining processes using genetic algorithms,” Int. J. Mach. Tools Manuf., vol. 47, no. 6, pp. 900–919, 2007. DOI: 10.1016/j.ijmachtools.2006.08.001.
   Web of Science ®Google Scholar
• R. N. Yadav, V. Yadava, and G. K. Singh, “Application of ANN-NSGA-II hybrid methodology for modeling and optimization of electrical discharge diamond face grinding of tungsten carbide-cobalt (WC-Co) composite,” J. Mach. Form. Technol., vol. 4, pp. 187–206, 2012.
   Google Scholar
• R. Tiwari, K. K. Sunil, and R. S. Reddy, “An optimal design methodology of tapered roller bearings using genetic algorithm,” Int. J. Comp. Methods Eng. Sci. Mech., vol. 13, no. 2, pp. 108–127, 2012. DOI: 10.1080/15502287.2011.654375.
   Google Scholar
• J. S. Rao and R. Tiwari, “A pareto optimal design analysis of magnetic thrust bearings using multi-objective genetic algorithm,” Int. J. Comput. Methods Eng. Sci. Mech., vol. 16, no. 2, pp. 71–87, 2015. DOI: 10.1080/15502287.2015.1009577.
   Web of Science ®Google Scholar
• T. D. B. Kannan, B. S. Kumar, G. R. Kannan, M. Umar, and M. C. Khan, “Application of genetic algorithm technique for machining parameters optimization in drilling of stainless steel,” Mech. Mech. Eng., vol. 23, no. 1, pp. 271–276, 2019. DOI: 10.2478/mme-2019-0036.
   Google Scholar
• S. Kumar, A. K. Gupta, and P. Chandna, “State of art optimization techniques for machining parameters optimization during milling,” Int. J. Eng. Adv. Technol., vol. 8, pp. 5104–5114, 2019.
   Google Scholar
• G. Daniel, G. Kumar, and A. H. Rizvi, “Optimization of material removal rate in wire-EDM using genetic algorithms,” In. J. Appl. Eng. Res., vol. 14, pp. 313–315, 2019.
   Google Scholar
• P. K. Shrivastava, B. Singh, Y. Shrivastava, A. K. Pandey, and D. Nandan, “Investigation of optimal process parameters for laser cutting of Inconel-718 sheet,” Proc. IMechE Part C: J. Mech. Eng. Sci., vol. 234, no. 8, pp. 1581–1597, 2020. DOI: 10.1177/0954406219895533.
   Web of Science ®Google Scholar
• D. Singh and R. Shukla, “Multi-objective optimization of selected non-traditional machining processes using NSGA-II,” 10.5267/j.dsl, vol. 9, pp. 421–438, 2020. DOI: 10.5267/j.dsl.2020.3.003.
   Google Scholar
• K. Deb, A. Pratap, S. Agarwal, and T. Meyarivan, “A fast and elitist multiobjective genetic algorithm: NSGA-II,” IEEE Trans. Evol. Comput., vol. 6, no. 2, pp. 182–197, 2002. DOI: 10.1109/4235.996017.
   Web of Science ®Google Scholar
• L. Song, “NGPM–A NSGA-II program in MATLAB, version 1.4, file exchange, Mathworks,” 2015. Available: http://www.mathworks.com/matlabcentral/fileexchange.
   Google Scholar
• P. J. Ross, Taguchi Techniques for Quality Engineering, 2nd ed. New York: McGraw Hill, 2005.
   Google Scholar
• J. Antony, “Simultaneous optimisation of multiple quality characteristics in processes using Taguchi’s quality loss,” Int. J. Adv. Manuf. Technol., vol. 17, no. 2, pp. 134–138, 2001. DOI: 10.1007/s001700170201.
   Web of Science ®Google Scholar
• D. C. Montgomery, Design and Analysis of Experiments. New Delhi, India: Wiley, 2013.
   Google Scholar