Modelling and multiobjective optimization for productivity improvement in high speed milling of Ti–6Al–4V using RSM and GA

  • Neelesh Kumar Sahu
  • Atul B. Andhare
Technical Paper


Productivity can be improved in machining by achieving higher material removal rate (MRR) and better surface finish at lower power consumption along with higher tool life. Present work focuses on analyzing power consumption, material removal rate; surface roughness and tool wear in high speed milling of Ti–6Al–4V using response surface methodology. Models are developed with experimental data measured after performing face milling operation sequentially using design of experiments. Developed models are validated and reformed using Analysis of variance (ANOVA) and stepwise backward elimination method. Developed models showed correlation coefficient (R 2) more than 95% which means models can best explain the experimental data. Further, multiobjective optimization is performed to minimize power consumption; surface roughness and tool wear as well as to maximize MRR using response optimizer with desirability approach. Optimum process parameters obtained are: cutting speed = 133.5 m/min, feed rate = 0.14 mm/tooth and depth of cut = 2.33 mm. Validation of optimized results is done with three confirmation experiments at the optimum conditions and the responses are taken as average of the three confirmation experiments. Additionally, Pareto optimal points are found for conflicting objective functions using multiobjective genetic algorithm.


Ti–6Al–4V Power consumption Surface roughness MRR Response surface methodology 


  1. 1.
    Ezugwu EO, Wang ZM (1997) Titanium alloys and their machinability—a review. J Mater Process Technol 68(3):262–274. doi: 10.1016/S0924-0136(96)00030-1 CrossRefGoogle Scholar
  2. 2.
    Pramanik A (2014) Problems and solutions in machining of titanium alloys. Int J Adv Manuf Technol 70(5):919–928. doi: 10.1007/s00170-013-5326-x CrossRefGoogle Scholar
  3. 3.
    Zhong Q, Tang R, Lv J, Jia S, Jin M (2016) Evaluation on models of calculating energy consumption in metal cutting processes: a case of external turning process. Int J Adv Manuf Technol 82(9):2087–2099. doi: 10.1007/s00170-015-7477-4 CrossRefGoogle Scholar
  4. 4.
    Swat M, Rebschläger A, Trapp K, Stock T, Seliger G, Bähre D (2015) Investigating the energy consumption of the PECM process for consideration in the selection of manufacturing process chains. Proced CIRP 29:585–590. doi: 10.1016/j.procir.2015.02.173 CrossRefGoogle Scholar
  5. 5.
    Duflou JR, Sutherland JW, Dornfeld D, Herrmann C, Jeswiet J, Kara S, Hauschild M, Kellens K (2012) Towards energy and resource efficient manufacturing: a processes and systems approach. CIRP Ann Manuf Technol 61(2):587–609. doi: 10.1016/j.cirp.2012.05.002 CrossRefGoogle Scholar
  6. 6.
    Pervaiz S, Deiab I, Darras B (2013) Power consumption and tool wear assessment when machining titanium alloys. Int J Precis Eng Manuf 2013:6. doi: 10.1007/s12541-013-0122-y Google Scholar
  7. 7.
    Mativenga PT, Rajemi MF (2011) Calculation of optimum cutting parameters based on minimum energy footprint. CIRP Ann Manuf Technol 60(1):149–152. doi: 10.1016/j.cirp.2011.03.088 CrossRefGoogle Scholar
  8. 8.
    Ma J, Ge X, Chang SI, Lei S (2014) Assessment of cutting energy consumption and energy efficiency in machining of 4140 steel. Int J Adv Manuf Technol 74(9):1701–1708. doi: 10.1007/s00170-014-6101-3 CrossRefGoogle Scholar
  9. 9.
    Balogun VA, Edem IF, Adekunle AA, Mativenga PT (2016) Specific energy based evaluation of machining efficiency. J Clean Prod 116:187–197. doi: 10.1016/j.jclepro.2015.12.106 CrossRefGoogle Scholar
  10. 10.
    Zhou L, Li J, Li F, Meng Q, Li J, Xu X (2016) Energy consumption model and energy efficiency of machine tools: a comprehensive literature review. J Clean Prod Part 5 112:3721–3734. doi: 10.1016/j.jclepro.2015.05.093 CrossRefGoogle Scholar
  11. 11.
    Denkena B, Dittrich MA, Jacob S (2016) Energy efficiency in machining of aircraft components. Proced CIRP 48:479–482. doi: 10.1016/j.procir.2016.03.155 CrossRefGoogle Scholar
  12. 12.
    Denkena B, Helmecke P, Hülsemeyer L (2015) Energy efficient machining of Ti–6Al–4V. CIRP Ann Manuf Technol 64(1):61–64. doi: 10.1016/j.cirp.2015.04.056 CrossRefGoogle Scholar
  13. 13.
    Deiab I, Raza SW, Pervaiz S (2014) Analysis of lubrication strategies for sustainable machining during turning of titanium Ti-6Al-4V alloy. Proced CIRP 17:766–771. doi: 10.1016/j.procir.2014.01.112 CrossRefGoogle Scholar
  14. 14.
    Wang Z, Nakashima S, Larson M (2014) Energy efficient machining of titanium alloys by controlling cutting temperature and vibration. Proced CIRP 17:523–528. doi: 10.1016/j.procir.2014.01.134 CrossRefGoogle Scholar
  15. 15.
    Pervaiz S, Deiab I, Rashid A, Nicolescu M, Kishawy H (2013) Energy consumption and surface finish analysis of machining Ti-6Al-4V. In: Proceedings of World Academy of Science, Engineering and Technology, vol 76. World Academy of Science, Engineering and Technology (WASET) 113–118Google Scholar
  16. 16.
    Pervaiz S, Deiab I, Rashid A, Nicolescu M (2015) Prediction of energy consumption and environmental implications for turning operation using finite element analysis. Proc Inst Mech Eng Part B J Eng Manuf 229(11):1925–1932. doi: 10.1177/0954405414541105 CrossRefGoogle Scholar
  17. 17.
    Sahu NK, Andhare A (2015) Optimization of surface roughness in turning of Ti-6Al-4V Using Response Surface Methodology and TLBO. In: ASME 2015 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, 2015. American Society of Mechanical Engineers V004T005A020–V004T005A020Google Scholar
  18. 18.
    Ulutan D, Ozel T (2011) Machining induced surface integrity in titanium and nickel alloys: a review. Int J Mach Tools Manuf 51(3):250–280. doi: 10.1016/j.ijmachtools.2010.11.003 CrossRefGoogle Scholar
  19. 19.
    Rahman M, Wong YS, Zareena AR (2003) Machinability of titanium alloys. JSME Int J Ser C 46(1):107–115. doi: 10.1299/jsmec.46.107 CrossRefGoogle Scholar
  20. 20.
    Nabhani F (2001) Machining of aerospace titanium alloys. Robotics Comput Integr Manuf 17(1–2):99–106. doi: 10.1016/S0736-5845(00)00042-9 CrossRefGoogle Scholar
  21. 21.
    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 192–193:147–158. doi: 10.1016/j.jmatprotec.2007.04.095 CrossRefGoogle Scholar
  22. 22.
    Su Y, He N, Li L, Li XL (2006) An experimental investigation of effects of cooling/lubrication conditions on tool wear in high-speed end milling of Ti-6Al-4V. Wear 261(7–8):760–766. doi: 10.1016/j.wear.2006.01.013 CrossRefGoogle Scholar
  23. 23.
    Zhang S, Li J, Sun J, Jiang F (2010) Tool wear and cutting forces variation in high-speed end-milling Ti-6Al-4V alloy. Int J Adv Manuf Technol 46(1–4):69–78CrossRefGoogle Scholar
  24. 24.
    Myers RH, Montgomery DC, Anderson-Cook CM (2009) Response surface methodology: process and product optimization using designed experiments, 3rd edn. Wiley, HobokenzbMATHGoogle Scholar
  25. 25.
    Mathai VJ, Dave HK, Desai KP (2016) Experimental investigations on EDM of Ti-6Al-4V with planetary tool actuation. J Braz Soc Mech Sci Eng 1–24. doi: 10.1007/s40430-016-0657-9
  26. 26.
    Che-Haron CH, Jawaid A (2005) The effect of machining on surface integrity of titanium alloy Ti–6% Al–4% V. J Mater Process Technol 166(2):188–192. doi: 10.1016/j.jmatprotec.2004.08.012 CrossRefGoogle Scholar
  27. 27.
    Shokrani A, Dhokia V, Newman ST (2016) Investigation of the effects of cryogenic machining on surface integrity in CNC end milling of Ti–6Al–4V titanium alloy. J Manuf Process 21:172–179. doi: 10.1016/j.jmapro.2015.12.002 CrossRefGoogle Scholar
  28. 28.
    Ramesh S, Karunamoorthy L, Palanikumar K (2008) Surface roughness analysis in machining of titanium alloy. Mater Manuf Process 23(2):174–181. doi: 10.1080/10426910701774700 CrossRefGoogle Scholar
  29. 29.
    Çelik YH, Yildiz H, Özek C (2016) Effect of cutting parameters on workpiece and tool properties during drilling of Ti-6Al-4V. Mater Test 58(6):519–525. doi: 10.3139/120.110886 CrossRefGoogle Scholar
  30. 30.
    Davoodi B, Tazehkandi AH (2014) Cutting forces and surface roughness in wet machining of Inconel alloy 738 with coated carbide tool. Proc Inst Mech Eng Part B J Eng Manuf 230(2):215–226CrossRefGoogle Scholar
  31. 31.
    Cakir MC, Ensarioglu C, Demirayak I (2009) Mathematical modeling of surface roughness for evaluating the effects of cutting parameters and coating material. J Mater Process Technol 209(1):102–109. doi: 10.1016/j.jmatprotec.2008.01.050 CrossRefGoogle Scholar
  32. 32.
    Chen L, El-Wardany TI, Harris WC (2004) Modelling the effects of flank wear land and chip formation on residual stresses. CIRP Ann Manuf Technol 53(1):95–98. doi: 10.1016/S0007-8506(07)60653-2 CrossRefGoogle Scholar
  33. 33.
    Çelik YH, Kilickap E, Güney M (2016) Investigation of cutting parameters affecting on tool wear and surface roughness in dry turning of Ti-6Al-4V using CVD and PVD coated tools. J Braz Soc Mech Sci Eng 1–9. doi: 10.1007/s40430-016-0607-6
  34. 34.
    Ginting A, Nouari M (2009) Surface integrity of dry machined titanium alloys. Int J Mach Tools Manuf 49(3–4):325–332. doi: 10.1016/j.ijmachtools.2008.10.011 CrossRefGoogle Scholar
  35. 35.
    Ramesh S, Karunamoorthy L, Palanikumar K (2012) Measurement and analysis of surface roughness in turning of aerospace titanium alloy (gr5). Measurement 45(5):1266–1276. doi: 10.1016/j.measurement.2012.01.010 CrossRefGoogle Scholar
  36. 36.
    Wang ZG, Wong YS, Rahman M (2005) High-speed milling of titanium alloys using binderless CBN tools. Int J Mach Tools Manuf 45(1):105–114. doi: 10.1016/j.ijmachtools.2004.06.021 CrossRefGoogle Scholar
  37. 37.
    Kumar KVBSK, Choudhury SK (2008) Investigation of tool wear and cutting force in cryogenic machining using design of experiments. J Mater Process Technol 203(1–3):95–101. doi: 10.1016/j.jmatprotec.2007.10.036 CrossRefGoogle Scholar
  38. 38.
    Choudhary AK, Chelladurai H, Kannan C (2015) Optimization of combustion performance of bioethanol (Water Hyacinth) diesel blends on diesel engine using response surface methodology. Arab J Sci Eng 40(12):3675–3695. doi: 10.1007/s13369-015-1810-y CrossRefGoogle Scholar
  39. 39.
    Sarve AN, Varma MN, Sonawane SS (2015) Response surface optimization and artificial neural network modeling of biodiesel production from crude mahua (Madhuca indica) oil under supercritical ethanol conditions using CO2 as co-solvent. RSC Adv 5(85):69702–69713. doi: 10.1039/C5RA11911A CrossRefGoogle Scholar

Copyright information

© The Brazilian Society of Mechanical Sciences and Engineering 2017

Authors and Affiliations

  1. 1.Department of Mechanical EngineeringVNITNagpurIndia

Personalised recommendations