Details
Title
Hybrid GRA-PCA and modified weighted TOPSIS coupled with Taguchi for multi-response process parameter optimization in turning AISI 1040 steelJournal title
Archive of Mechanical EngineeringYearbook
2021Volume
vol. 68Issue
No 1Affiliation
Sultana, Mst. Nazma : Bangladesh University of Engineering & Technology, Dhaka, Bangladesh. ; Dhar, Nikhil Ranjan : Bangladesh University of Engineering & Technology, Dhaka, Bangladesh.Authors
Keywords
grey relational analysis ; principal component analysis ; Taguchi method ; analysis of variance ; cryogenic coolingDivisions of PAS
Nauki TechniczneCoverage
23-49Publisher
Polish Academy of Sciences, Committee on Machine BuildingBibliography
[1] S.S. Nair, T. Ramkumar, M. Selva Kumar, and F. Netto. Experimental investigation of dry turning of AISI 1040 steel with TiN coated insert. Engineering Research Express, 1(2):1–13, 2019. doi: 10.1088/2631-8695/ab58d9.[2] M.N. Sultana, N.R. Dhar, and P.B. Zaman. A Review on different cooling/lubrication techniques in metal cutting. American Journal of Mechanics and Applications, 7(4):71–87, 2019. doi: 10.11648/j.ajma.20190704.11.
[3] M.N. Sultana, P.B. Zaman, and N.R. Dhar. GRA-PCA coupled with Taguchi for optimization of inputs in turning under cryogenic cooling for AISI 4140 steel. Journal of Production Systems & Manufacturing Science, 1(2):40–62, 2020.
[4] M. Mia. Multi-response optimization of end milling parameters under through-tool cryogenic cooling condition. Measurement, 111:134–145, 2017. doi: 10.1016/j.measurement. 2017.07.033.
[5] L.S. Ahmed, N. Govindaraju, and M. Pradeep Kumar. Experimental investigations on cryogenic cooling in the drilling of titanium alloy. Materials and Manufacturing Processes, 31(5):603–607, 2016. doi: 10.1080/10426914.2015.1019127.
[6] A.B. Chattopadhyay, A. Bose, and A.K. Chattopdhyay. Improvements in grinding steels by cryogenic cooling. Precision Engineering, 7(2):93–98, 1985. doi: 10.1016/0141-6359(85)90098-4.
[7] P.P. Reddy and A. Ghosh. Some critical issues in cryo-grinding by a vitrified bonded alumina wheel using liquid nitrogen jet. Journal of Materials Processing Technology, 229: 29–337, 2016. doi: 10.1016/j.jmatprotec.2015.09.040.
[8] M. Vijay Kumar, B.J. Kiran Kumar, and N. Rudresha. Optimization of machining parameters in CNC turning of stainless steel (EN19) by Taguchi’s orthogonal array experiments. Materials Today: Proceedings, 5(5):11395–11407, 2018. doi: 10.1016/j.matpr.2018.02.107.
[9] M. Mia and N.R. Dhar. Optimization of surface roughness and cutting temperature in high-pressure coolant-assisted hard turning using Taguchi method. The International Journal of Advanced Manufacturing Technology, 88(1-4):739–753, 2017. doi: 10.1007/s00170-016-8810-2.
[10] G.M. Patel, Jagadish, R. Suresh Kumar, and N.V.S. Naidu. Optimization of abrasive water jet machining for green composites using multi-variant hybrid techniques. In K.Gupta, M.Kumar Gupta (eds.) Optimization of Manufacturing Processes, pages 129–162, Springer, 2020. doi: 10.1007/978-3-030-19638-7_6.
[11] D. Saravanakumar, B. Mohan, and T. Muthuramalingam. Application of response surface methodology on finding influencing parameters in servo pneumatic system. Measurement, 54:40–50, 2014. doi: 10.1016/j.measurement.2014.04.017.
[12] N.S. Jaddi and S. Abdullah. A cooperative-competitive master-slave global-best harmony search for ANN optimization and water-quality prediction. Applied Soft Computing, 51:209–224, 2017. doi: 10.1016/j.asoc.2016.12.011.
[13] A.S. Prasanth, R. Ramesh, and G. Palaniappan. Taguchi grey relational analysis for multi-response optimization of wear in co-continuous composite. Materials, 11(9):1743, 2018. doi: 10.3390/ma11091743.
[14] R. Manivannan and M.Pradeep Kumar. Multi-attribute decision-making of cryogenically cooled micro-EDM drilling process parameters using TOPSIS method. Materials and Manufacturing Processes, 32(2):209–215, 2017. doi: 10.1080/10426914.2016.1176182.
[15] J.S. Vesterstrøm and J. Riget. Particle swarms: Extensions for improved local, multi-modal, and dynamic search in numerical optimization. Master's Thesis, Dept. of Computer Science, University of Aarhus, Denmark, May, 2002.
[16] G. Meral, M. Sarıkaya, M. Mia, H. Dilipak, U. Şeker, and M.K. Gupta. Multi-objective optimization of surface roughness, thrust force, and torque produced by novel drill geometries using Taguchi-based GRA. The International Journal of Advanced Manufacturing Technology, 101(5-8):1595–1610, 2019. doi: 10.1007/s00170-018-3061-z.
[17] M. Priyadarshini, I. Nayak, J. Rana and P.P. Tripathy. Multi-objective optimization of turning process using fuzzy-TOPSIS analysis. Materials Today: Proceedings, March, 2020. doi: 10.1016/j.matpr.2020.02.847.
[18] M. Alhabo and L. Zhang. Multi-criteria handover using modified weighted TOPSIS methods for heterogeneous networks. IEEE Access, 6:40547–40558, 2018. doi: 10.1109/ACCESS.2018.2846045.
[19] P.B. Zaman, S. Saha, and N.R. Dhar. Hybrid Taguchi-GRA-PCA approach for multi-response optimisation of turning process parameters under HPC condition. International Journal of Machining and Machinability of Materials, 22(3-4):281–308, 2020. doi: 10.1504/IJMMM.2020.107059.
[20] N. Li, Y.J. Chen, and D.D. Kong. Multi-response optimization of Ti-6Al-4V turning operations using Taguchi-based grey relational analysis coupled with kernel principal component analysis. Advances in Manufacturing, 7(2):142–154, 2019. doi: 10.1007/s40436-019-00251-8.
[21] P. Umamaheswarrao, D.R. Raju, K.N.S. Suman, and B.R. Sankar. Multi objective optimization of process parameters for hard turning of AISI 52100 steel using Hybrid GRA-PCA. Procedia Computer Science, 133:703–710, 2018. doi: 10.1016/j.procs.2018.07.129.
[22] P.B. Patole and V.V. Kulkarni. Experimental investigation and optimization of cutting parameters with multi response characteristics in MQL turning of AISI 4340 using nano fluid. Cogent Engineering, 4(1):1303956, 2017. doi: 10.1080/23311916.2017.1303956.
[23] R. Viswanathan, S. Ramesh, S. Maniraj, and V. Subburam. Measurement and multi-response optimization of turning parameters for magnesium alloy using hybrid combination of Taguchi-GRA-PCA technique. Measurement, 159:107800, 2020. doi: 10.1016/ j.measurement.2020.107800.
[24] S. Ramesh, R. Viswanathan and S. Ambika. Measurement and optimization of surface roughness and tool wear via grey relational analysis, TOPSIS and RSA techniques. Measurement, 78:63–72, 2016. doi: 10.1016/j.measurement.2015.09.036.
[25] A. Palanisamy and T. Selvaraj. Optimization of turning parameters for surface integrity properties on Incoloy 800H superalloy using cryogenically treated multi-layer CVD coated tool. Surface Review and Letters, 26(02):1850139, 2019. doi: 10.1142/S0218625X18501391.
[26] R. Thirumalai and J.S. Senthilkumaar. Multi-criteria decision making in the selection of machining parameters for Inconel 718. Journal of Mechanical Science and Technology, 27(4):1109–1116, 2013. doi: 10.1007/s12206-013-0215-7.
[27] M. Mia. Mathematical modeling and optimization of MQL assisted end milling characteristics based on RSM and Taguchi method. Measurement, 121:249–260, 2018. doi: 10.1016/j.measurement.2018.02.017.
[28] P.J. Ross. Taguchi Techniques for Quality Engineering. McGraw-Hill, New York, 2 edition, 1996.
[29] A. Palanisamy and T. Selvaraj. Optimization of machining parameters for dry turning of Incoloy 800H using Taguchi-based grey relational analysis. Materials Today: Proceedings, 5(2):7708–7715, 2018. doi: 10.1016/j.matpr.2017.11.447.
[30] K. Pearson. On lines and planes of closest fit to systems of points in space. The London, Edinburgh, and Dublin Philosophical Magazine and Journal of Science, 2(11):559–572, 1901. doi: 10.1080/14786440109462720.
[31] H. Hotelling. Analysis of a complex of statistical variables into principal components. Journal of Educational Psychology, 24(6):417–441, 1993. doi: 10.1037/h0071325.
[32] M. Mia, M.K. Gupta, J.A. Lozano, D. Carou, D.Y. Pimenov, G. Królczyk, A.M. Khan, and N.R. Dhar. Multi-objective optimization and life cycle assessment of eco-friendly cryogenic N 2 assisted turning of Ti-6Al-4V. Journal of Cleaner Production, 210: 121-133, 2019. doi: 10.1016/j.jclepro.2018.10.334.
[33] M.A. Khan, S.H.I. Jaffery, M. Khan, M. Younas, S.I. Butt, R. Ahmad, and S.S. Warsi. Multi-objective optimization of turning titanium-based alloy Ti-6Al-4V under dry, wet, and cryogenic conditions using gray relational analysis (GRA). The International Journal of Advanced Manufacturing Technology, 106(9-10):3897–3911, 2020. doi: 10.1007/s00170-019-04913-6.
[34] M.J. Bermingham, J. Kirsch, S. Sun, S. Palanisamy, and M.S. Dargusch. New observations on tool life, cutting forces and chip morphology in cryogenic machining Ti-6Al-4V. International Journal of Machine Tools and Manufacture, 51(6):500–511, 2011. doi: 10.1016/j.ijmachtools.2011.02.009.
[35] M. Strano, E. Chiappini, S. Tirelli, P. Albertelli, and M. Monno. Comparison of Ti6Al4V machining forces and tool life for cryogenic versus conventional cooling. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, 227(9):1403–1408, 2013. doi: 10.1177/0954405413486635.
[36] H.S. Lu, C.K. Chang, N.C. Hwang, and C.T. Chung. Grey relational analysis coupled with principal component analysis for optimization design of the cutting parameters in high-speed end milling. Journal of Materials Processing Technology, 209(8):3808–3817, 2009. doi: 10.1016/j.jmatprotec.2008.08.030.
[37] L.S. Ahmed and M.Pradeep Kumar. Multiresponse optimization of cryogenic drilling on Ti-6Al-4V alloy using TOPSIS method. Journal of Mechanical Science and Technology, 30(4):1835–1841, 2016. doi: 10.1007/s12206-016-0340-1.