Advertisement

LBM of aluminum alloy: towards a control of material removal and roughness

  • Naveed AhmedEmail author
  • Salman Pervaiz
  • Shafiq Ahmad
  • Madiha Rafaqat
  • Adeel Hassan
  • Mazen Zaindin
ORIGINAL ARTICLE
  • 128 Downloads

Abstract

Achieving the maximum material removal rate (MRRmax) is not always desired in machining especially during laser milling. Actual volume of the material removed during laser beam machining (LBM) is not always precisely equal to the designed volume. Dimensional accuracy of the laser milled feature requires the controlled layer of the substrate removal after each scanning cycle so that the cumulative material removal after full length of canning cycle conforms to the designed depth or geometry. In this research, laser milling of aluminum alloy has been carried out. Percentage of material removal rate (MRR%) and the roughness of the machined surface (SR) are taken as the response indicators. Optimal parametric combinations resulting in MRR% close to 100% with minimum SR have been pursued. Strength of the effects of five significant variables (in terms of one-way, square, and two-way interactions) is also identified. Furthermore, mathematical models are developed to predict the machining responses prior to proceed for actual machining. The research outcomes may be utilized to perform laser milling of AA 2024 (aluminum alloy used in various fields including aerospace industry) with precise control over MRR which ultimately will strengthen the dimensional accuracy of the machined profiles.

Keywords

Laser beam milling AA 2024 MRR% MRRth Surface roughness Optimization Mathematical models Scanning Layer thickness 

Notes

Acknowledgments

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.
    Mandal KK, Kuar AS, Mitra S (2018) Experimental investigation on laser micro-machining of Al 7075 Alloy. Opt Laser Technol 107:260–267CrossRefGoogle Scholar
  2. 2.
    Smith C, Liu Q, Zhuang W (2014) An experimental study of initial flaws in thin sheets of 2024 aircraft aluminium alloy. Int J Struct Integr 5(2):120–128CrossRefGoogle Scholar
  3. 3.
    Jahan MP, Kakavand P, Kwang ELM, Rahman M, Wong YS (2015) An experimental investigation into the micro-electro-discharge machining behaviour of aluminium alloy (AA 2024). Int J Adv Manuf Technol 78(5):1127–1139CrossRefGoogle Scholar
  4. 4.
    Haddag B, Atlati S, Nouari M, Moufki A (2016) Dry machining aeronautical aluminum alloy AA2024-T351: analysis of cutting forces, chip segmentation and built-up edge formation. Metals 6(9):197CrossRefGoogle Scholar
  5. 5.
    Rawangwong S, Chatthong J, Boonchouytan W, Homkhiew C, Cheewawuttipong W, Burapa R (2017) Influence of cutting parameters in face milling semi-solid AA 2024 using a carbide tool affecting the surface roughness and tool wear. Walailak J Sci Technol 14(6):441–449Google Scholar
  6. 6.
    Pattnaik SK, Bhoi NK, Padhi S, Sarangi SK (2018) Dry machining of aluminum for proper selection of cutting tool: tool performance and tool wear. Int J Adv Manuf Technol 98(1):55–65CrossRefGoogle Scholar
  7. 7.
    Ran Z et al (2014) Laser-machined microcavities for simultaneous measurement of high-temperature and high-pressure. Sensors 14(8):14330–14338CrossRefGoogle Scholar
  8. 8.
    Martínez Vázquez R et al (2017) Rapid prototyping of plastic lab-on-a-chip by femtosecond laser micromachining and removable insert microinjection molding. Micromachines 8(11):328CrossRefGoogle Scholar
  9. 9.
    Lee W-H, Ozel T (2009) An experimental method for laser micro-machining of spherical and elliptical 3-D objects. Int J Nanomanuf 3(3):264–278CrossRefGoogle Scholar
  10. 10.
    Hung Y-H, Chien H-L, Lee Y-C (2018) Excimer laser three-dimensional micromachining based on image projection and the optical diffraction effect. Appl Sci 8(9):1690CrossRefGoogle Scholar
  11. 11.
    Paula KT, Mercante LA, Schneider R, Correa DS, Mendonca CR (2019) Micropatterning MoS2/polyamide electrospun nanofibrous membranes using femtosecond laser pulses. Photonics 6(1):3CrossRefGoogle Scholar
  12. 12.
    Kadhim A, Salim ET, Fayadh SM, Al-Amiery AA, Kadhum AAH, Mohamad AB (2014) Effect of multipath laser shock processing on microhardness, surface roughness, and wear resistance of 2024-T3 Al alloy. Sci World J [Online]. Available: https://www.hindawi.com/journals/tswj/2014/490951/. [Accessed: 01-Mar-2019]
  13. 13.
    Hussein HT, Kadhim A, Al-Amiery AA, Kadhum AAH, Mohamad AB (2014) Enhancement of the wear resistance and microhardness of aluminum alloy by Nd:YaG laser treatment. Sci World J [Online]. Available: https://www.hindawi.com/journals/tswj/2014/842062/. [Accessed: 01-Mar-2019]
  14. 14.
    Shen Z, Liu H, Wang X, Wang H (2011) Micromold-based laser shock embossing of metallic foil: fabrication of large-area three-dimensional microchannel networks. Mater Manuf Process 26(9):1126–1129CrossRefGoogle Scholar
  15. 15.
    Lu G, Li J, Zhang Y, Sokol DW (2019) A metal marking method based on laser shock processing. Mater Manuf Process 0(0):1–6Google Scholar
  16. 16.
    Lei S, Yang G, Wang X, Chen S, Prieb A, Ma J (2018) High energy femtosecond laser peening of 2024 aluminum alloy. Procedia CIRP 74:357–361CrossRefGoogle Scholar
  17. 17.
    Ahmmed KMT, Grambow C, Kietzig A-M (2014) Fabrication of micro/nano structures on metals by femtosecond laser micromachining. Micromachines 5(4):1219–1253CrossRefGoogle Scholar
  18. 18.
    Biswas S, Karthikeyan A, Kietzig A-M (2016) Effect of repetition rate on femtosecond laser-induced homogenous microstructures. Materials 9(12):1023CrossRefGoogle Scholar
  19. 19.
    Benton M, Hossan MR, Konari PR, Gamagedara S (2019) Effect of process parameters and material properties on laser micromachining of microchannels. Micromachines 10(2):123CrossRefGoogle Scholar
  20. 20.
    Rao ACU, Vasu V, Shariff S m, Srinadh KVS (2016) Influence of diode laser surface melting on microstructure and corrosion resistance of 7075 aluminium alloy. Int J Microstruct Mater Prop 11(1–2):85–104Google Scholar
  21. 21.
    Hedieh P (2018) Theoretical and experimental investigations of the influence of overlap between the laser beam tracks on channel profile and morphology in pulsed laser machining of polymers. Optik 171:431–436CrossRefGoogle Scholar
  22. 22.
    Mukherjee R, Goswami D, Chakraborty S (2013) Parametric optimization of Nd:YAG laser beam machining process using artificial bee colony algorithm. J Ind Eng [Online]. Available: https://www.hindawi.com/journals/jie/2013/570250/. [Accessed: 01-Mar-2019]
  23. 23.
    Sharma A, Yadava V (2013) Simultaneous optimisation of average kerf taper and surface roughness during pulsed Nd: YAG laser cutting of thin Al-alloy sheet for straight profile. Int J Manuf Technol Manag 27(1–3):112–126CrossRefGoogle Scholar
  24. 24.
    Dubey AK, Yadava V (2008) Optimization of kerf quality during pulsed laser cutting of aluminium alloy sheet. J Mater Process Technol 204(1):412–418CrossRefGoogle Scholar
  25. 25.
    Kuar A s, Paul G, Mitra S (2006) Nd:YAG laser micromachining of alumina–aluminium interpenetrating phase composite using response surface methodology. Int J Mach Mach Mater 1(4):432–444Google Scholar
  26. 26.
    Kuar A s, Dhara S k, Mitra S (2010) Multi-response optimisation of Nd:YAG laser micro-machining of die steel using response surface methodology. Int J Manuf Technol Manag 21(1–2):17–29CrossRefGoogle Scholar
  27. 27.
    Ahuir-Torres JI, Arenas MA, Perrie W, de Damborenea J (2018) Influence of laser parameters in surface texturing of Ti6Al4V and AA2024-T3 alloys. Opt Lasers Eng 103:100–109CrossRefGoogle Scholar
  28. 28.
    Ahuir-Torres JI, Arenas MA, Perrie W, Dearden G, de Damborenea J (2017) Surface texturing of aluminium alloy AA2024-T3 by picosecond laser: effect on wettability and corrosion properties. Surf Coat Technol 321:279–291CrossRefGoogle Scholar
  29. 29.
    Agrawal D, Kamble D (2019) Optimization of photochemical machining process parameters for manufacturing microfluidic channel. Mater Manuf Process 34(1):1–7CrossRefGoogle Scholar
  30. 30.
    Teixidor D, Ciurana J, Rodríguez C (2013) Multiobjective optimization of laser milling parameters of microcavities for the manufacturing of DES. Mater Manuf Process 28(12):1370–1378CrossRefGoogle Scholar
  31. 31.
    Gupta MK, Sood PK, Sharma VS (2016) Machining parameters optimization of titanium alloy using response surface methodology and particle swarm optimization under minimum-quantity lubrication environment. Mater Manuf Process 31(13):1671–1682CrossRefGoogle Scholar
  32. 32.
    Aydar AY (2018) Utilization of response surface methodology in optimization of extraction of plant materials. Statistical approaches with emphasis on design of experiments applied to chemical processesGoogle Scholar
  33. 33.
    Saba F, Raygan S (2017) Application of response surface methodology for modelling of TiC coating on AISI D2 steel using a mechanical milling technique. Powder Metall 60(4):280–292CrossRefGoogle Scholar
  34. 34.
    Lin Y, Huang J, Wei J, Liao X, Xiao Z (2018) Modeling and optimization of high-grade compacted graphite iron milling force and surface roughness via response surface methodology. Aust J Mech Eng 16(1):50–57CrossRefGoogle Scholar
  35. 35.
    Yousuff CM, Danish M, Ho ETW, Kamal Basha IH, Hamid NHB (2017) Study on the optimum cutting parameters of an aluminum mold for effective bonding strength of a PDMS microfluidic device. Micromachines 8(8):258CrossRefGoogle Scholar
  36. 36.
    Danish M, Ginta TL, Habib K, Carou D, Rani AMA, Saha BB (2017) Thermal analysis during turning of AZ31 magnesium alloy under dry and cryogenic conditions. Int J Adv Manuf Technol 91(5):2855–2868CrossRefGoogle Scholar
  37. 37.
    Song H, Ren G, Dan J, Li J, Xiao J, Xu J (2018) Experimental study of the cutting force during laser-assisted machining of fused silica based on artificial neural network and response surface methodology. SiliconGoogle Scholar

Copyright information

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

Authors and Affiliations

  1. 1.Department of Industrial and Manufacturing EngineeringUniversity of Engineering and TechnologyLahorePakistan
  2. 2.Department of Mechanical and Industrial EngineeringRochester Institute of TechnologyDubaiUnited Arab Emirates
  3. 3.College of Engineering, Department of Industrial EngineeringKing Saud UniversityRiyadhSaudi Arabia
  4. 4.Department of Mechanical EngineeringUniversity of LahoreLahorePakistan
  5. 5.College of Science, Department of Statistics and Operations ResearchKing Saud UniversityRiyadhSaudi Arabia

Personalised recommendations