Multi-response optimization of Ti-6Al-4V turning operations using Taguchi-based grey relational analysis coupled with kernel principal component analysis

Abstract

Ti-6Al-4V has a wide range of applications, especially in the aerospace field; however, it is a difficult-to-cut material. In order to achieve sustainable machining of Ti-6Al-4V, multiple objectives considering not only economic and technical requirements but also the environmental requirement need to be optimized simultaneously. In this work, the optimization design of process parameters such as type of inserts, feed rate, and depth of cut for Ti-6Al-4V turning under dry condition was investigated experimentally. The major performance indexes chosen to evaluate this sustainable process were radial thrust, cutting power, and coefficient of friction at the tool-chip interface. Considering the nonlinearity between the various objectives, grey relational analysis (GRA) was first performed to transform these indexes into the corresponding grey relational coefficients, and then kernel principal component analysis (KPCA) was applied to extract the kernel principal components and determine the corresponding weights which showed their relative importance. Eventually, kernel grey relational grade (KGRG) was proposed as the optimization criterion to identify the optimal combination of process parameters. The results of the range analysis show that the depth of cut has the most significant effect, followed by the feed rate and type of inserts. Confirmation tests clearly show that the modified method combining GRA with KPCA outperforms the traditional GRA method with equal weights and the hybrid method based on GRA and PCA.

Keywords

Ti-6Al-4V / Taguchi method / Grey relational analysis (GRA) / Kernel principal component analysis (KPCA) / Multi-response optimization

References

[1.] Zhou L, Zhao YQ, Wang XD. Study on development strategy of titanium alloy materials and application in China, 2012, Beijing: Chemical Industry Press

[2.] Çolak O. Optimization of machining performance in high-pressure assisted turning of Ti6Al4V alloy. Stroj Vestn-J Mech Eng, 2014, 60(10): 675-681.

[3.] Chinchanikar S, Choudhury SK. Effect of work material hardness and cutting parameters on performance of coated carbide tool when turning hardened steel: an optimization approach. Measurement, 2013, 46: 1572-1584.

[4.] Amin AKMN, Ismail AF, Khairusshima MKN. Effectiveness of uncoated WC-Co and PCD inserts in end milling of titanium alloy—Ti-6Al-4V. J Mater Process Technol, 2007, 192(5): 147-158.

[5.] Sun FJ, Qu SG, Pan YX, et al. Effects of cutting parameters on dry machining Ti-6Al-4V alloy with ultra-hard tools. Int J Adv Manuf Technol, 2015, 79(1–4): 351-360.

[6.] Wu KZ, Chen YJ, Zhu DD, et al. Application of friction-reducing groove on insert with 3D chip-breaking groove. Tool Technol, 2005, 39(5): 53-55.

[7.] Arulkirubakaran D, Senthilkumar V, Kumawat V. Effect of micro-textured tools on machining of Ti-6Al-4V alloy: an experimental and numerical approach. Int J Refract Met Hard Mater, 2016, 54: 165-177.

[8.] Li N, Chen YJ, Kong DD, et al. Experimental investigation with respect to the performance of deep submillimeter-scaled textured tools in dry turning titanium alloy Ti-6Al-4V. Appl Surf Sci, 2017, 403: 187-199.

[9.] Pusavec F, Krajnik P, Kopac J. Transitioning to sustainable production-Part I: application on machining technologies. J Clean Prod, 2010, 18(2): 174-184.

[10.] Pusavec F, Krajnik P, Kopac J. Transitioning to sustainable production-Part II: evaluation of sustainable machining technologies. J Clean Prod, 2010, 18(12): 1211-1221.

[11.] Malakooti B, Wang J, Tandler EC. A sensor-based accelerated approach for multi-attribute machinability and tool life evaluation. Int J Prod Res, 1990, 28(12): 2373-2392.

[12.] Subramanian M, Sakthivel M, Sooryaprakash K, et al. Optimization of end mill tool geometry parameters for Al7075-T6 machining operations based on vibration amplitude by response surface methodology. Measurement, 2013, 46(10): 4005-4022.

[13.] Singh D, Rao PV. Optimization of tool geometry and cutting parameters for hard turning. Mater Manuf Process, 2007, 22(1): 15-21.

[14.] Ramana MV, Rao GKM, Rao DH. Optimization and effect of process parameters on tool wear in turning of titanium alloy under different machining conditions. Int J Mater Mech Manuf, 2014, 2(4): 272-277.

[15.] Yan JH, Li L. Multi-objective optimization of milling parameters—the trade-offs between energy, production rate and cutting quality. J Clean Prod, 2013, 52: 462-471.

[16.] Sarıkaya M, Güllü A. Multi-response optimization of minimum quantity lubrication parameters using Taguchi-based grey relational analysis in turning of difficult-to-cut alloy Haynes 25. J Clean Prod, 2015, 91: 347-357.

[17.] Thepsonthi T, Özel T. Multi-objective process optimization for micro-end milling of Ti-6Al-4V titanium alloy. Int J Adv Manuf Technol, 2012, 63(9–12): 903-914.

[18.] Yi Q, Li CB, Tang Y, et al. Multi-objective parameter optimization of CNC machining for low carbon manufacturing. J Clean Prod, 2015, 95: 256-264.

[19.] Nayak SK, Patro JK, Dewangan S, et al. Multi-objective optimization of machining parameters during dry turning of AISI 304 austenitic stainless steel using grey relational analysis. Procedia Mater Sci, 2014, 6: 701-708.

[20.] Mia M, Khan MA, Rahman SS, et al. Mono-objective and multi-objective optimization of performance parameters in high pressure coolant assisted turning of Ti-6Al-4V. Int J Adv Manuf Technol, 2017, 90(1–4): 109-118.

[21.] Mia M, Khan MA, Dhar NR. Study of surface roughness and cutting forces using ANN, RSM, and ANOVA in turning of Ti-6Al-4V under cryogenic jets applied at flank and rake faces of coated WC tool. Int J Adv Manuf Technol, 2017, 93(1–4): 975-991.

[22.] Xiong LS, Yan XG, Zhang FR. Fundamentals of mechanical manufacturing technology, 2006, Wuhan: Huazhong University of Science and Technology Press

[23.] Newman ST, Nassehi A, Asrai RI, et al. Energy efficient process planning for CNC machining. CIRP J Manuf Sci Technol, 2012, 5(2): 127-136.

[24.] Mia M, Rifat A, Tanvir MF, et al. Multi-objective optimization of chip-tool interaction parameters using grey-Taguchi method in MQL-assisted turning. Measurement, 2018, 129: 156-166.

[25.] Zhang TY, Owodunni O, Gao J. Scenarios in multi-objective optimization of process parameters for sustainable machining. Procedia CIRP, 2015, 26: 373-378.

[26.] Hanafi I, Khamlichi A, Cabrera FM, et al. Optimization of cutting conditions for sustainable machining of PEEK-CF30 using TiN tools. J Clean Prod, 2012, 33: 1-9.

[27.] Bhushan RK. Optimization of cutting parameters for minimizing power consumption and maximizing tool life during machining of Al alloy SiC particle composites. J Clean Prod, 2013, 39: 242-254.

[28.] Lu HS, Chang CK, Hwang NC, et al. Grey relational analysis coupled with principal component analysis for optimization design of the cutting parameters in high-speed end milling. J Mater Process Technol, 2009, 209: 3808-3817.

[29.] Dubey AK, Yadava V. Multi-objective optimization of Nd:YAG laser cutting of nickel-based superalloy sheet using orthogonal array with principal component analysis. Opt Lasers Eng, 2008, 46(2): 124-132.

[30.] Ross PJ. Taguchi techniques for quality engineering, 1988, New York: McGraw-Hill

[31.] Deng JL. The course of grey system theory, 1982, Wuhan: Huazhong University of Science and Technology Press

Funding

National Science and Technology Major Project of China(2012ZX04003-021)