Advertisement

Machinability of titanium alloy through laser machining: material removal and surface roughness analysis

  • Naveed AhmedEmail author
  • Shafiq Ahmad
  • Saqib Anwar
  • Amjad Hussain
  • Madiha Rafaqat
  • Mazen Zaindin
ORIGINAL ARTICLE
  • 53 Downloads

Abstract

Laser milling is a competent precision process especially when the work material is hard-to-machine such as titanium alloys. While performing the laser milling, a slight change in one of the laser parameters results in an abrupt change in the machining outcomes. A close match between the designed and the machined geometries is the essence of precision machining. A precise control over the material removal rate per laser scan is highly desirable but difficult to achieve. The difficulty level becomes higher if high surface finish is desired alongside the precision machining. In this research, the objective was set to perform the laser milling on titanium alloy (Ti-6Al-4V) with 100% control over material removal rate (MRR) per laser scan and minimum surface roughness (SR). Influence of the five laser parameters (laser intensity, pulse frequency, scan speed, layer thickness, and track displacement) on MRR and SR has been deeply investigated. Significance of each laser parameter is evaluated through ANOVA. Mathematical models for both the responses are developed to estimate the resulting responses at any parametric setting. Models have also been validated through confirmatory tests. Optimization of laser parameters is of great importance to remove the material exactly equal to the desired depth with minimum surface roughness. Therefore, the optimized combinations of laser parameters have been proposed which ensure the conformance of 100% MRR and minimum surface roughness with composite desirability > 0.9. Confirmatory experiments revealed that the optimized parameters are capable to produce the laser milling results as per the models’ predicted results. Additionally, the microstructure of the subsequent layers below the milled area has also been examined and compared with the microstructure of the bulk Ti-6Al-4V. By the use of optimized parameters, microstructure of the sub-layers remains unchanged as compared with the microstructure of the base metal. No evidence has been found altering the microstructure of the sub-layers.

Keywords

Laser milling Titanium alloy Material removal rate (MRR) Surface roughness (SR) Mathematical model Optimization 

Notes

Acknowledgements

The authors extend their appreciation to the Deanship of Scientific Research at King Saud University for funding this work through research group no. (RG- 1438-089).

References

  1. 1.
    Yang Y, Su Y, Li L, He N, Zhao W (2015) Performance of cemented carbide tools with microgrooves in Ti-6Al-4V titanium alloy cutting. Int J Adv Manuf Technol 76(9):1731–1738CrossRefGoogle Scholar
  2. 2.
    Mhatre MS, Sapkal SU, Pawade RS (2014) Electro discharge machining characteristics of Ti-6Al-4V alloy: a grey relational optimization. Procedia Mater Sci 5:2014–2022CrossRefGoogle Scholar
  3. 3.
    Nourbakhsh F, Rajurkar KP, Malshe AP, Cao J (2013) Wire electro-discharge machining of titanium alloy. Procedia CIRP 5:13–18CrossRefGoogle Scholar
  4. 4.
    Churi NJ, Pei ZJ, Treadwell C (2006) Rotary ultrasonic machining of titanium alloy: effects of machining variables. Mach Sci Technol 10(3):301–321CrossRefGoogle Scholar
  5. 5.
    Xu Z, Chen X, Zhou Z, Qin P, Zhu D (2016) Electrochemical machining of high-temperature titanium alloy Ti60. Procedia CIRP 42:125–130CrossRefGoogle Scholar
  6. 6.
    Venkatesan K, Ramanujam R, Kuppan P (2014) Laser assisted machining of difficult to cut materials: research opportunities and future directions - a comprehensive review. Process Eng 97:1626–1636Google Scholar
  7. 7.
    de Lacalle LN L’p, Sa’nchez JA, Lamikiz A, Celaya A (2004) Plasma assisted milling of heat-resistant superalloys. J Manuf Sci Eng 126(2):274–285CrossRefGoogle Scholar
  8. 8.
    Roy A, Silberschmidt VV (2014) Ultrasonically assisted machining of titanium alloys. In: Davim JP (ed) Machining of titanium alloys. Springer Berlin Heidelberg, Berlin, pp 131–147Google Scholar
  9. 9.
    Tabernero I, Lamikiz A, Martínez S, Ukar E, Lacalle LNLD (2012) Geometric modelling of added layers by coaxial laser cladding. Phys Procedia 39:913–920CrossRefGoogle Scholar
  10. 10.
    Tabernero I, Lamikiz A, Martínez S, Ukar E, López de Lacalle LN (2012) Modelling of energy attenuation due to powder flow-laser beam interaction during laser cladding process. J Mater Process Technol 212(2):516–522CrossRefGoogle Scholar
  11. 11.
    Darwish SMH, Ahmed N, Al-Ahmari AMA (2017) Laser beam micro-milling of micro-channels. In: Aerospace alloys. Springer, SingaporeGoogle Scholar
  12. 12.
    Büttner H, Hajri M, Roth R, Wegener K (2018) High aspect ratio microstructuring of copper surfaces by means of ultrashort pulse laser ablation. Procedia CIRP 68:190–195CrossRefGoogle Scholar
  13. 13.
    Borse SC, Kadam MS (2018) Experimental study in micromilling of Inconel 718 by fiber laser machining. Proc Manuf 20:213–218Google Scholar
  14. 14.
    Umer U, Mohammed MK, Al-Ahmari A (2017) Multi-response optimization of machining parameters in micro milling of alumina ceramics using Nd:YAG laser. Measurement 95:181–192CrossRefGoogle Scholar
  15. 15.
    Vora HD, Santhanakrishnan S, Harimkar SP, Boetcher SKS, Dahotre NB (2013) One-dimensional multipulse laser machining of structural alumina: evolution of surface topography. Int J Adv Manuf Technol 68(1–4):69–83CrossRefGoogle Scholar
  16. 16.
    Genna S, Tagliaferri F, Papa I, Leone C, Palumbo B (2016) Experimental investigation on CFRP milling by low power Q-switched Yb:YAG laser source. AIP Conference Proceedings 1736(1):020159CrossRefGoogle Scholar
  17. 17.
    Yang C-C et al (2017) Laser-induced coloring of titanium alloy using ultraviolet nanosecond pulses scanning technology. J Alloys Compd 715:349–361CrossRefGoogle Scholar
  18. 18.
    Kalita K, Shivakoti I, Ghadai RK (2017) Optimizing process parameters for laser beam micro-marking using genetic algorithm and particle swarm optimization. Mater Manuf Process 32(10):1101–1108CrossRefGoogle Scholar
  19. 19.
    Mishra S, Yadava V (2013) Prediction of material removal rate due to laser beam percussion drilling in aluminium sheet using the finite element method. Int J Mach Mach Mater 14(4):342Google Scholar
  20. 20.
    Puoza JC, Hua X, Liu Q, Kang Z, Zhang P (2018) Manufacturing of micro-textures on metals by nanosecond laser micromachining. Adv Mater Process Technol 4(1):86–99Google Scholar
  21. 21.
    Ghosal A, Patil P (2017) Machining parameters optimization during machining of Al/5 wt% alumina metal matrix composite by fiber laser. AIP Conference Proceedings 1851(1):020082CrossRefGoogle Scholar
  22. 22.
    Lim GC, Tan MAC, Tan CW, Zhong HY (2010) Effects of laser fluence on the micro-structure formation and material removal rate in ablation of silicon carbide. ICALEO 2010(1):799–805Google Scholar
  23. 23.
    Mohammed MK, Umer U, Al-Ahmari A (2017) Optimization of laser micro milling of alumina ceramic using radial basis functions and MOGA-II. Int J Adv Manuf Technol 91(5–8):2017–2029CrossRefGoogle Scholar
  24. 24.
    Schille J et al (2011) Micro processing of metals using a high repetition rate femtosecond laser: from laser process parameter study to machining examples. ICALEO 2011(1):773–782Google Scholar
  25. 25.
    Zhu H, Zhang Z, Xu J, Xu K, Ren Y (2018) An experimental study of micro-machining of hydroxyapatite using an ultrashort picosecond laser. Precis Eng 54:154–162CrossRefGoogle Scholar
  26. 26.
    Schille J, Schneider L, Loeschner U (2015) Process optimization in high-average-power ultrashort pulse laser microfabrication: how laser process parameters influence efficiency, throughput and quality. Appl Phys A Mater Sci Process 120(3):847–855CrossRefGoogle Scholar
  27. 27.
    Williams E, Brousseau EB, Rees A (2014) Nanosecond Yb fibre laser milling of aluminium: effect of process parameters on the achievable surface finish and machining efficiency. Int J Adv Manuf Technol 74(5):769–780CrossRefGoogle Scholar
  28. 28.
    Mandal KK, Kuar AS, Mitra S (2018) Experimental investigation on laser micro-machining of Al 7075 Alloy. Opt Laser Technol 107:260–267CrossRefGoogle Scholar
  29. 29.
    Wee LM, Lim GC, Zheng HY (2011) Dimensional analyses and surface quality of pulsed UV laser micro-machining of STAVAX stainless steel mold inserts. Int J Adv Manuf Technol 57(9):1011–1027CrossRefGoogle Scholar
  30. 30.
    Ogawa K, Nakagawa H, Imada T, Tanabe H (2018) Laser irradiation method for high-efficiency pulsed laser milling integrated die/mold machining: effects of laser-tracking irradiation on laser milling accuracy. Adv Mater Process Technol 4(1):158–165Google Scholar
  31. 31.
    Kumar J, Khamba JS (2010) Modeling the material removal rate in ultrasonic machining of titanium using dimensional analysis. Int J Adv Manuf Technol 48(1):103–119CrossRefGoogle Scholar
  32. 32.
    Yu Z, Yang G, Zhang W, Hu J (2018) Investigating the effect of picosecond laser texturing on microstructure and biofunctionalization of titanium alloy. J Mater Process Technol 255:129–136CrossRefGoogle Scholar
  33. 33.
    Ghosal A, Manna A (2013) Response surface method based optimization of ytterbium fiber laser parameter during machining of Al/Al2O3-MMC. Opt Laser Technol 46:67–76CrossRefGoogle Scholar
  34. 34.
    Hossain A et al (2016) A fuzzy logic-based prediction model for kerf width in laser beam machining. Mater Manuf Process 31(5):679–684CrossRefGoogle Scholar

Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2019

Authors and Affiliations

  • Naveed Ahmed
    • 1
    • 2
    Email author
  • Shafiq Ahmad
    • 2
  • Saqib Anwar
    • 2
  • Amjad Hussain
    • 1
  • Madiha Rafaqat
    • 1
  • Mazen Zaindin
    • 3
  1. 1.Department of Industrial and Manufacturing EngineeringUniversity of Engineering and TechnologyLahorePakistan
  2. 2.Department of Industrial Engineering, College of EngineeringKing Saud UniversityRiyadhSaudi Arabia
  3. 3.Department of Statistics and Operations Research, College of ScienceKing Saud UniversityRiyadhSaudi Arabia

Personalised recommendations