Ti-6Al-4V has a wide range of applications, especially in the aerospace field; however, it is a difficultto-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 toolchip 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.
The full text can be downloaded at https://link.springer.com/article/10.1007/s40436-019-00251-8
Ning Li
,
Yong-Jie Chen
,
Dong-Dong Kong
. Multi-response optimization of Ti-6Al-4V turning operations using Taguchi-based grey relational analysis coupled with kernel principal component analysis[J]. Advances in Manufacturing, 2019
, 7(2)
: 142
-154
.
DOI: 10.1007/s40436-019-00251-8
1. Zhou L, Zhao YQ, Wang XD (2012) Study on development strategy of titanium alloy materials and application in China. Chemical Industry Press, Beijing
2. Çolak O (2014) Optimization of machining performance in highpressure assisted turning of Ti6Al4V alloy. Stroj Vestn-J Mech Eng 60(10):675-681
3. Chinchanikar S, Choudhury SK (2013) Effect of work material hardness and cutting parameters on performance of coated carbide tool when turning hardened steel:an optimization approach. Measurement 46:1572-1584
4. Amin AKMN, Ismail AF, Khairusshima MKN (2007) Effectiveness of uncoated WC-Co and PCD inserts in end milling of titanium alloy-Ti-6Al-4V. J Mater Process Technol 192(5):147-158
5. Sun FJ, Qu SG, Pan YX et al (2015) Effects of cutting parameters on dry machining Ti-6Al-4V alloy with ultra-hard tools. Int J Adv Manuf Technol 79(1-4):351-360
6. Wu KZ, Chen YJ, Zhu DD et al (2005) Application of frictionreducing groove on insert with 3D chip-breaking groove. Tool Technol 39(5):53-55
7. Arulkirubakaran D, Senthilkumar V, Kumawat V (2016) Effect of micro-textured tools on machining of Ti-6Al-4V alloy:an experimental and numerical approach. Int J Refract Met Hard Mater 54:165-177
8. Li N, Chen YJ, Kong DD et al (2017) Experimental investigation with respect to the performance of deep submillimeter-scaled textured tools in dry turning titanium alloy Ti-6Al-4V. Appl Surf Sci 403:187-199
9. Pusavec F, Krajnik P, Kopac J (2010) Transitioning to sustainable production-Part I:application on machining technologies. J Clean Prod 18(2):174-184
10. Pusavec F, Krajnik P, Kopac J (2010) Transitioning to sustainable production-Part Ⅱ:evaluation of sustainable machining technologies. J Clean Prod 18(12):1211-1221
11. Malakooti B, Wang J, Tandler EC (1990) A sensor-based accelerated approach for multi-attribute machinability and tool life evaluation. Int J Prod Res 28(12):2373-2392
12. Subramanian M, Sakthivel M, Sooryaprakash K et al (2013) Optimization of end mill tool geometry parameters for Al7075-T6 machining operations based on vibration amplitude by response surface methodology. Measurement 46(10):4005-4022
13. Singh D, Rao PV (2007) Optimization of tool geometry and cutting parameters for hard turning. Mater Manuf Process 22(1):15-21
14. Ramana MV, Rao GKM, Rao DH (2014) Optimization and effect of process parameters on tool wear in turning of titanium alloy under different machining conditions. Int J Mater Mech Manuf 2(4):272-277
15. Yan JH, Li L (2013) Multi-objective optimization of milling parameters-the trade-offs between energy, production rate and cutting quality. J Clean Prod 52:462-471
16. Sarıkaya M, Güllü A (2015) 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 91:347-357
17. Thepsonthi T, Özel T (2012) Multi-objective process optimization for micro-end milling of Ti-6Al-4V titanium alloy. Int J Adv Manuf Technol 63(9-12):903-914
18. Yi Q, Li CB, Tang Y et al (2015) Multi-objective parameter optimization of CNC machining for low carbon manufacturing. J Clean Prod 95:256-264
19. Nayak SK, Patro JK, Dewangan S et al (2014) Multi-objective optimization of machining parameters during dry turning of AISI 304 austenitic stainless steel using grey relational analysis. Procedia Mater Sci 6:701-708
20. Mia M, Khan MA, Rahman SS et al (2017) Mono-objective and multi-objective optimization of performance parameters in high pressure coolant assisted turning of Ti-6Al-4V. Int J Adv Manuf Technol 90(1-4):109-118
21. Mia M, Khan MA, Dhar NR (2017) 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 93(1-4):975-991
22. Xiong LS, Yan XG, Zhang FR (2006) Fundamentals of mechanical manufacturing technology. Huazhong University of Science and Technology Press, Wuhan
23. Newman ST, Nassehi A, Asrai RI et al (2012) Energy efficient process planning for CNC machining. CIRP J Manuf Sci Technol 5(2):127-136
24. Mia M, Rifat A, Tanvir MF et al (2018) Multi-objective optimization of chip-tool interaction parameters using grey-Taguchi method in MQL-assisted turning. Measurement 129:156-166
25. Zhang TY, Owodunni O, Gao J (2015) Scenarios in multi-objective optimization of process parameters for sustainable machining. Procedia CIRP 26:373-378
26. Hanafi I, Khamlichi A, Cabrera FM et al (2012) Optimization of cutting conditions for sustainable machining of PEEK-CF30 using TiN tools. J Clean Prod 33:1-9
27. Bhushan RK (2013) Optimization of cutting parameters for minimizing power consumption and maximizing tool life during machining of Al alloy SiC particle composites. J Clean Prod 39:242-254
28. Lu HS, Chang CK, Hwang NC et al (2009) Grey relational analysis coupled with principal component analysis for optimization design of the cutting parameters in high-speed end milling. J Mater Process Technol 209:3808-3817
29. Dubey AK, Yadava V (2008) Multi-objective optimization of Nd:YAG laser cutting of nickel-based superalloy sheet using orthogonal array with principal component analysis. Opt Lasers Eng 46(2):124-132
30. Ross PJ (1988) Taguchi techniques for quality engineering. McGraw-Hill, New York
31. Deng JL (1982) The course of grey system theory. Huazhong University of Science and Technology Press, Wuhan
