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A Combined CFD, ANFIS and NSGA-II Model for Repetitive Pulse Laser Drilling Process

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Abstract

The article is focused on maximizing the dimensions of a hole drilled through repetitive pulsed laser drilling process by employing the artificial neuro-fuzzy inference system (ANFIS) and multi-objective Genetic Algorithm (GA) with the help of the data obtained from numerical modelling. An axisymmetric two-dimensional numerical model is developed for studying this repetitive pulse laser drilling process in a cylindrical work piece to determine the drilled hole depth and radius and the temperature at the point of laser application. The governing differential equations are discretized using the Finite volume method and the Tri-Diagonal Matrix Algorithm is used to solve the resulting algebraic equations to obtain temperature distribution inside the computational domain. The Enthalpy-Porosity method is used to track the solid-liquid interface during the melting process. The laser source considered here is Gaussian and volumetric in nature and the computations are carried out taking different laser parameters like energy, number of pulses, pulse width, duty cycle and beam radius. The artificial intelligence method ANFIS is adopted to model the process using the result data obtained from numerical investigation. Separate ANFIS models are developed for the prediction of the depth, radius, center temperature, HAZ and taperness of the simulated drilled hole. These models are used as objective functions for a multi-objective genetic algorithm program to get a set optimal solutions for maximizing the depth and radius of the drilled hole and the temperature at the point of laser application in an objective to achieve highest material removal. Another section is also presented carrying out the multi-objective optimisation of considering maximiisation of radius, depth and center temperature along with minimisation of the HAZ and the tapoerness of the hole.

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Abbreviations

ap :

co-efficient in the discretized algebraic equation

α 0 :

radius of laser beam

Cp :

specific heat

g:

gravitational acceleration

g L :

volume fraction of liquid

hsen :

sensible heat

h:

convective coefficient of heat transfer

H:

enthalpy

E:

laser energy

Io :

peak intensity of laser beam

k:

thermal conductivity

Lf :

latent heat of fusion

ms:

millisecond

SLS :

laser source term

Sh :

enthalpy source term

Sr :

r-momentum source term

Sz :

z-momentum source term

T:

temperature

t:

time

ton :

on time

toff :

off time

u:

z-component of velocity

v:

r-component of velocity

r,z :

cylindrical coordinate directions

N:

number of pulses

V:

resultant velocity

mf:

membership function

a, b, c:

parameters used in ANFIS modelling

κ:

absorption coefficient

α:

thermal diffusivity

λ:

under-relaxation parameter

β:

volumetric thermal expansion coefficient

ρ:

density

μ:

dynamic viscosity

σ:

Stefan-Boltzmann constant

τ:

pulse width

Δt:

time step size

ΔH :

nodal latent heat

ΔV:

volume of a control volume

m:

melting point

v:

boiling point

P:

control volume

nb:

neighbouring nodes

ref:

reference

0:

initial time when t = 0

n:

number of iteration

o:

previous time step

References

  1. Nakhjavani, O.B., Ghoreishi, M.: Multi criteria optimization of laser percussion drilling process using artificial neural network model combined with genetic algorithm. Mater. Manuf. Process. 21(1), 11–18 (2006)

    Article  Google Scholar 

  2. Olabi, A.G., Casalino, G., Benyounis, K.Y., Hashmi, M.S.J.: An ANN and Taguchi algorithms integrated approach to the optimization of CO2laser welding. Adv. Eng. Softw. 37(10), 643–648 (2006)

    Article  Google Scholar 

  3. Park, Y.W., Rhee, S.: Process modeling and parameter optimization using neural network and genetic algorithms for aluminum laser welding automation. Int. J. Adv. Manuf. Technol. 37(9-10), 1014–1021 (2008)

    Article  Google Scholar 

  4. Dhara, S.K., Kuar, A.S., Mitra, S.: An artificial neural network approach on parametric optimization of laser micro-machining of die-steel. Int. J. Adv. Manuf. Technol. 39(1-2), 39–46 (2008)

    Article  Google Scholar 

  5. Assarzadeh, S., Ghoreishi, M.: Neural-network-based modeling and optimization of the electro-discharge machining process. Int. J. Adv. Manuf. Technol. 39(5-6), 488–500 (2008)

    Article  Google Scholar 

  6. Missori, S., Sili, A., Ucciardello, N.: Process parameters optimization of laser beam welded joints by neural network. Mater. Manuf. Process. 23, 169–174 (2008)

    Article  Google Scholar 

  7. Ghoreishi, M., Nakhjavani, O.B.: Optimisation of effective factors in geometrical specifications of laser percussion drilled holes. J. Mater. Process. Technol. 196(1-3), 303–310 (2008)

    Article  Google Scholar 

  8. Dhupal, D., Doloi, B., Bhattacharyya, B.: Modeling and optimization on Nd:YAG laser turned micro-grooving of cylindrical ceramic material. Opt. Lasers Eng. 47(9), 917–925 (2009)

    Article  Google Scholar 

  9. Karazi, S.M., Issa, A., Brabazon, D.: Comparison of ANN and DoE for the prediction of laser-machined micro-channel dimensions. Opt. Lasers Eng. 47(9), 956–964 (2009)

    Article  Google Scholar 

  10. Sharma, A., Yadava, V.: Modelling and optimization of cut quality during pulsed Nd:YAG laser cutting of thin Al-alloy sheet for straight profile. Opt. Laser Technol. 44(1), 159–168 (2012)

    Article  Google Scholar 

  11. Kumar Pandey, A., Kumar Dubey, A.: Simultaneous optimization of multiple quality characteristics in laser cutting of titanium alloy sheet. Opt. Laser Technol. 44(6), 1858–1865 (2012)

    Article  Google Scholar 

  12. Mishra, S., Yadava, V.: Modeling and optimization of laser beam percussion drilling of thin aluminum sheet. Opt. Laser Technol. 48, 461–474 (2013)

    Article  Google Scholar 

  13. Goyal, R., Dubey, A.K.: Quality improvement by parameter optimization in laser trepan drilling of superalloy sheet. Mater. Manuf. Process. 29(11-12), 1410–1416 (2014)

    Article  Google Scholar 

  14. Sohrabpoor, H.: Analysis of laser powder deposition parameters: ANFIS modeling and ICA optimization. Optik (Stuttg). 127, 4031–4038 (2016)

    Article  Google Scholar 

  15. Akbari, M., Saedodin, S., Panjehpour, A., Hassani, M., Afrand, M., Torkamany, M.J.: Numerical simulation and designing artificial neural network for estimating melt pool geometry and temperature distribution in laser welding of Ti6Al4V alloy. Optik (Stuttg). 127, 11161–11172 (2016)

    Article  Google Scholar 

  16. Seabra, M., Azevedo, J., Araújo, A., Reis, L., Pinto, E., Alves, N., Santos, R., Pedro Mortágua, J.: Selective laser melting (SLM) and topology optimization for lighter aerospace componentes. Proc Struct Integr. 1, 289–296 (2016)

    Article  Google Scholar 

  17. Bai, Y., Yang, Y., Xiao, Z., Zhang, M., Wang, D.: Process optimization and mechanical property evolution of AlSiMg0.75 by selective laser melting. Mater. Des. 140, 257–266 (2018)

    Article  Google Scholar 

  18. Mao, Z., Zhang, D.Z., Jiang, J., Fu, G., Zhang, P.: Processing optimisation, mechanical properties and microstructural evolution during selective laser melting of Cu-15Sn high-tin bronze. Mater. Sci. Eng. A. 721, 125–134 (2018)

    Article  Google Scholar 

  19. Basu, B., Srinivasan, J.: Numerical study of steady-state laser melting problem. Int. J. Heat Mass Transf. 31(11), 2331–2338 (1988)

    Article  Google Scholar 

  20. Kar, A., Mazumder, J.: Two-dimensional model for material damage due to melting and vaporization during laser irradiation. J. Appl. Phys. 68(8), 3884–3891 (1990)

    Article  Google Scholar 

  21. Zhu, X., Naumov, A.Y., Villeneuve, D.M., Corkum, P.B.: Influence of laser parameters and material properties on micro drilling with femtosecond laser pulses. Appl. Phys. A Mater. Sci. Process. 69, 367–371 (1999)

    Article  Google Scholar 

  22. Yilbas, B.S., Naqvi, I.Z.: Laser heating including the phase change process and thermal stress generation in relation to drilling. Proc. Inst. Mech. Eng. Part B J. Eng. Manuf. 217(7), 977–991 (2003)

    Article  Google Scholar 

  23. Shuja, S.Z., Yilbas, B.S., Khan, S.M.A.: Laser consecutive pulse heating in relation to melting: influence of duty cycle on melting. Heat Mass Transf. 45(6), 793–803 (2009)

    Article  Google Scholar 

  24. Qin, Y., Wang, B., Ni, X.W., Shen, Z.H., Lu, J.: Axisymmetric analytical solution for pulsed laser melting of metals. Comput. Mater. Sci. 54, 1–6 (2012)

    Article  Google Scholar 

  25. Afrin, N., Ji, P., Chen, J.K., Zhang, Y.: Effects of beam size and pulse duration on the laser drilling process. (2016). https://doi.org/10.1115/HT2016-7339

  26. Chen, G., Wang, Y., Jhang, J., Bi, J.: An analytical solution for two-dimensional modeling of repetitive long pulse laser heating material. Int. J. Heat Mass Transf. 104, 503–509 (2017)

    Article  Google Scholar 

  27. Gautam, G.D., Pandey, A.K.: Pulsed Nd:YAG laser drilling : a review. Opt. Laser Technol. 100, 183–215 (2018)

    Article  Google Scholar 

  28. Safari, M., Farzin, M.: Experimental investigation of laser forming of a saddle shape with spiral irradiating scheme. Opt. Laser Technol. 66, 146–150 (2015)

    Article  Google Scholar 

  29. Safari, M., Mostaan, H., Farzin, M.: Laser bending of tailor machined blanks: effect of start point of scan path and irradiation direction relation to step of the blank. Alex. Eng. J. 55(2), 1587–1594 (2016)

    Article  Google Scholar 

  30. Safari, M., Farzin, M., Mostaan, H.: A novel method for laser forming of two-step bending of a dome shaped part. Iranian Journal of Materials Forming. 4, 1–14 (2017)

    Google Scholar 

  31. Safari, M., Joudaki, J.: Prediction of bending angle for laser forming of tailor machined blanks by neural network. 5, 47–57 (2018)

  32. Safari, M.: A study on the laser tube bending process: effects of the irradiating length and the number of irradiating passes. Iranian Journal of Materials Forming. 7, 46–53 (2020)

    Google Scholar 

  33. Bhardwaj, T., Shukla, M.: Laser additive manufacturing- direct energy deposition of Ti-15Mo biomedical alloy: artificial neural network based modeling of track dilution. Lasers Manuf. Mater. Process. 7(3), 245–258 (2020)

    Article  Google Scholar 

  34. Kochar, P., Sharma, A., Suga, T., Tanaka, M.: Prediction and control of asymmetric bead shape in laser-arc hybrid fillet-lap joints in sheet metal welds. Lasers Manuf. Mater. Process. 6(1), 67–84 (2019)

  35. Chaki, S., Bose, D., Bathe, R.N.: Multi-objective optimization of pulsed Nd: YAG laser cutting process using entropy-based ANN-PSO model. Lasers Manuf. Mater. Process. 7(1), 88–110 (2020)

    Article  Google Scholar 

  36. Saini, S.K., Dubey, A.K., Pant, P., Upadhyay, B.N., Choubey, A.: Study of laser drilled hole quality of Yttria stabilized zirconia. Lasers Manuf. Mater. Process. 4(3), 121–135 (2017)

  37. Tadamalle, A.P., Reddy, Y.P., Ramjee, E., Reddy, V.K.: Evaluation of distortion and residual stresses in dissimilar metal laser welds. Lasers Manuf. Mater. Process. 6(4), 374–386 (2019)

    Article  Google Scholar 

  38. Golabi, S., Vakil, M.R., Amirsalari, B.: Multi-objective optimization of residual stress and cost in laser shock peening process using finite element analysis and PSO algorithm. Lasers Manuf. Mater. Process. 6(4), 398–423 (2019)

    Article  Google Scholar 

  39. Farahmand, P., Kovacevic, R.: Parametric study and multi-criteria optimization in laser cladding by a high power direct diode laser. Lasers Manuf. Mater. Process. 1(1-4), 1–20 (2014)

    Article  Google Scholar 

  40. Patankar, S.V.: Numerical Heat Transfer and Fluid Flow, Hemisphere, London (1980)

  41. Brent, A.D., Voller, V.R., Reid, K.J.: Enthalpy-porosity technique for modeling convection - diffusion phase change: application to the melting of a pure metal. Numer. Heat Transf. 13, 297–318 (1988)

    Article  Google Scholar 

  42. Deb, K.: Multi-Objective Optimization Using Evolutionary Algorithms. Wiley, New York (2001)

    MATH  Google Scholar 

  43. Haupt, R.L., Haupt, S.E.: Practical Genetic Algorithms. Wiley, New Jersey (2004)

    MATH  Google Scholar 

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Authors and Affiliations

  1. School of Mechanical Engineering KIIT (Deemed to be University), Bhubaneswar, Odisha, 751024, India

    S. Sukumar, S. P. Kar, A. Swain, R. K. Sarangi & P. C. Sekhar

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  1. S. Sukumar
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  2. S. P. Kar
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  3. A. Swain
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Sukumar, S., Kar, S.P., Swain, A. et al. A Combined CFD, ANFIS and NSGA-II Model for Repetitive Pulse Laser Drilling Process. Lasers Manuf. Mater. Process. 7, 449–477 (2020). https://doi.org/10.1007/s40516-020-00127-x

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