, Volume 11, Issue 2, pp 691–701 | Cite as

A Novel Approach for Minimization of Tool Vibration and Surface Roughness in Orthogonal Turn Milling of Silicon Bronze Alloy

  • K Venkata RaoEmail author
Original Paper


The advanced manufacturing system is aimed to produce components at right quantity, quality and cost. Turnmilling is one of the advanced machining techniques that combines turning and milling processes for high metal removal rate. In orthogonal turn milling, bottom surface of the end mill cutter removes material from the surface of a rotating workpiece. Optimization of process parameters plays an important role in machining to improve quality and productivity and reduce production cost. In the present work, an advanced teaching learning based optimization (TLBO) technique was introduced to optimize process parameters in orthogonal turn milling of Silicon Bronze. Experiments were conducted at five levels of cutting speed, feed and depth of cuts. Experimental results of surface roughness and amplitude of cutter vibration were analysed using analysis of variance. The experimental results were also used to optimize process parameters through TLBO. Experiments were also conducted using TLBO optimized process parameters and the results were compared with TLBO results. The TLBO results were found to be in good agreement with target values of the responses. Artificial neural networks were developed for the surface roughness and amplitude of cutter vibration to verify optimization.


Turn milling Silicon bronze TLBO ANN Tool vibration Optimization 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.



This work (Major project) was funded by Science and Engineering Research Board, Department of Science and Technology, Government of India. Grant No.: SERB/F/1761/2015-16.


  1. 1.
    Milling Technical Guide. Sandvik Coromant, http://www.coromant. Accessed in May 2017
  2. 2.
    Yan R, Tang X, Peng FY, Wang Y, Qiu F (2016) The effect of variable cutting depth and thickness on milling stability for orthogonal turn-milling. Int J Adv Manuf Technol 82:765–777CrossRefGoogle Scholar
  3. 3.
    Ramaswamy N (1968) Koeningsberger Experiments with Self propelled rotary cutting tools. In: Proceedings of 9th IMTDR conference, Part, vol 2, pp 945–959Google Scholar
  4. 4.
    Savas V, Ozay C (2008) The optimization of the surface roughness in the process of tangential turn-milling using genetic algorithm. Int J Adv Manuf Technol 37:335–340CrossRefGoogle Scholar
  5. 5.
    Ren J, Zhou J, Zeng J (2015) Analysis and optimization of cutter geometric parameters for surface integrity in milling titanium alloy using a modified grey–Taguchi method. Proc Inst Mech Eng Part B: J Eng Manuf 230(11):2114–2128CrossRefGoogle Scholar
  6. 6.
    Sivasakthivel PS, Sudhakaran R, Rajeswari S (2013) Optimization of machining parameters to minimize vibration amplitude while machining Al 6063 using gray-based Taguchi method. Proc Inst Mech Eng Part B: J Eng Manuf 227(12):1788–1799CrossRefGoogle Scholar
  7. 7.
    Kono D, Moriya Y, Matsubara A (2017) Influence of rotary axis on tool-workpiece loop compliance for five-axis machine tools. Precision Eng 49:278–286CrossRefGoogle Scholar
  8. 8.
    Ratnam Ch, Vikram KA, Ben BS, Murthy BSN (2016) Process monitoring and effects of process parameters on responses in turn-milling operations based on SN ratio and ANOVA. Measurement 94:221–232CrossRefGoogle Scholar
  9. 9.
    Jalili MM, Hesabi J, Abootorabi MM (2017) Simulation of forced vibration in milling process considering gyroscopic moment and rotary inertia. Int J Adv Manuf Technol 89:2821–2836CrossRefGoogle Scholar
  10. 10.
    Zahoor S, Mufti NA, Saleem MQ, Mughal MP, Qureshi MAM (2017) Effect of machine tool’s spindle forced vibrations on surface roughness, dimensional accuracy, and tool wear in vertical milling of AISI p20. Int J Adv Manuf Technol 89:3671–3679CrossRefGoogle Scholar
  11. 11.
    Khalili K, Danesh M (2015) Identification of vibration level in metal cutting using undecimated wavelet transform and gray-level co-occurrence matrix texture features. Proc Inst Mech Eng Part B: J Eng Manuf 229(2):205–213CrossRefGoogle Scholar
  12. 12.
    Venkatarao K, Murthy BSN, Mohanrao N (2013) Cutting tool condition monitoring by analyzing surface roughness, work piece vibration and volume of metal removed for AISI 1040 steel in boring. Measurement 46:4075–4084CrossRefGoogle Scholar
  13. 13.
    Hessainia Z, Belbah A, Yallese MA, Mabrouki T, Rigal J-F (2013) On the prediction of surface roughness in the hard turning based on cutting parameters and tool vibrations. Measurement 46(5):1671–1681CrossRefGoogle Scholar
  14. 14.
    Subramanian M, Sakthivel M, Sooryaprakash K, Sudhakaran R (2013) Optimization of end mill tool geometry parameters for Al7075-T6 machining operations based on vibration amplitude by response surface methodology. Measurement 46(10):4005–4022CrossRefGoogle Scholar
  15. 15.
    Zhou J, Ren J, Yao C (2017) Multi-objective optimization of multi-axis ball-end milling Inconel 718 via grey relational analysis coupled with RBF neural network and PSO algorithm. Measurement 102:271–285CrossRefGoogle Scholar
  16. 16.
    Rao RV, Kalyankar VD (2013) Multi-pass turning process parameter optimization using teaching–learning-based optimization algorithm. Scientia Iranica E 20(3):967–974Google Scholar
  17. 17.
    Rao KV, Murthy PBGSN, Vidhu KP (2017) Assignment of weightage to machining characteristics to improve overall performance of machining using GTMA and utility concept CIRP. J Manuf Sci Technol.
  18. 18.
    Cheema MS, Dvivedi A, Sharma AK (2013) A hybrid approach to multicriteria optimization based on user’s preference rating. Proc I Mech E Part B: J Eng Manuf 227(11):1733–1742CrossRefGoogle Scholar
  19. 19.
    Rao RV (2015) Teaching learning based optimization algorithm and its engineering application, Springer publishing company, 2nd chap, 1st edn. ISBN: 3319227319 9783319227313Google Scholar
  20. 20.
    Rao KV, Murthy PBGSN (2016) Modeling and optimization of tool vibration and surface roughness in boring of steel using RSM. ANN and SVM. J Intell Manuf.
  21. 21.
    Yildiz AR (2013) Hybrid Taguchi-differential evolution algorithm for optimization of multi-pass turning operations. Appl Soft Comput 13(3):1433–1439CrossRefGoogle Scholar
  22. 22.
    Yildiz AR (2013) A new hybrid differential evolution algorithm for the selection of optimal machining parameters in milling operations. Appl Soft Comput 13(3):1561–1566CrossRefGoogle Scholar
  23. 23.
    Yildiz AR (2013) Optimization of multi-pass turning operations using hybrid teaching learning-based approach. Int J Adv Manuf Technol 66(9–12):1319–1326CrossRefGoogle Scholar
  24. 24.
  25. 25.
    Rao PS, Gupta OP, Murty SSN, Rao ABK (2009) Effect of process parameters and mathematical model for the prediction of bead geometry in pulsed GMA welding. Int J Adv Manuf Technol 45:496–505CrossRefGoogle Scholar
  26. 26.
    Sivaiah P, Chakradhar D (2018) Analysis and modeling of cryogenic turning operation using response surface methodology. Silicon.
  27. 27.
    Balaji M, Murthy BSN, Rao NM (2018) Multi response optimization of cutting parameters in drilling of AISI 304 stainless steels using response surface methodology. Proc I Mech E Part B: J Eng Manuf 232(1):151–161CrossRefGoogle Scholar
  28. 28.
    Prasad Babu GHV, Murthy BSN, Rao KV, Ratnam Ch (2017) Multi response optimization in orthogonal mill turning by analysing tool vibration and surface roughness using response surface methodology. Proc Inst Mech Eng Part B: J Eng Manuf 231(12):2084–2093CrossRefGoogle Scholar
  29. 29.
    Qu S, Sun F, Zhang L, Li X (2014) Effects of cutting parameters on dry cutting of aluminum bronze alloy. Int J Adv Manuf Technol 70(1–4):669–678CrossRefGoogle Scholar
  30. 30.
    Taha MA, El-Mahallawy NA, Hammouda RM, Moussa TM, Gheith MH (2012) Machinability characteristics of lead free-silicon brass alloys as correlated with microstructure and mechanical properties. Ain Shams Eng J 3:383–392CrossRefGoogle Scholar
  31. 31.
    Kianfar E, Shirshahi M, Kianfar F, Kianfar F (2018) Simultaneous prediction of the density, viscosity and electrical conductivity of pyridinium-based hydrophobic ionic liquids using artificial neural network. Silicon.
  32. 32.
    Khan A, Maity K (2018) A comprehensive GRNN model for the prediction of cutting force, surface roughness and tool wear during turning of CP-Ti grade 2. Silicon.

Copyright information

© Springer Nature B.V. 2018

Authors and Affiliations

  1. 1.Department of Mechanical EngineeringVignan’s Foundation for Science, Technology and Research Deemed to be UniversityVadlamudiIndia

Personalised recommendations