Advertisement

Laser Ablation of Cobalt-Bound Tungsten Carbide and Aluminium Oxide Ceramic: Experimental Investigation with ANN Modelling and GA Optimisation

  • Ahmed Elkaseer
  • Jon Lambarri
  • Iban Quintana
  • Steffen Scholz
Conference paper
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 130)

Abstract

This paper reports the results of an experimental investigation into the ablation of cobalt-bound tungsten carbide and aluminium oxide ceramic, two so-called super-composite materials, using a nanosecond pulsed laser. The ablation of single trenches was performed for different scan speeds and laser fluence. The ablation process was assessed in terms of the surface roughness along the centre lines of the trenches and the ablation rate. Using an Artificial Neural Network (ANN) and Genetic Algorithm (GA), a model was generated using the laser parameters as inputs and measured results as outputs. The optimal results predicted by the model were validated experimentally with a maximum difference of 14% between predicted values and measured results. The model correctly quantified the effect of the laser settings (fluence and scan speed) on surface roughness and ablation rate and identified the processing window and ablation conditions, for the optimum ablation performance of a nanosecond pulsed laser. Nevertheless, with the surface roughness of the aluminium oxide ceramic there was a noticeable difference of 32.7% between the prediction by the model and the results of the validation test, an exception to generally accurate predictions. Modelling of the laser ablation process reduces the number of trials during the setup stage, saving time and contributing to a more efficient and economically sustainable manufacturing process.

Keywords

ns pulsed laser ablation Ablation rate Surface roughness ANN GA 

Notes

Acknowledgements

This research was funded by the Steep project “A Synergetic Training Network on Energy Beam Processing: from modelling to industrial applications” within EC seventh framework program under the grant agreement no 316560.

The authors gratefully acknowledge the technical support of Dr Eva Rodriguez and Mr Jon Etxarri from IK4-Tekinker, Spain.

References

  1. 1.
    Cappelli, E., Orlando, S., Pinzari, F., Napoli, A., Kaciulis, S.: WC-Co cutting tool surface modifications induced by pulsed laser treatment. Appl. Surf. Sci. 138–139, 376–382 (1999)CrossRefGoogle Scholar
  2. 2.
    Garcia-Giron, A., Sola, D., Pena, J.: Liquid-assisted laser ablation of advanced ceramics and glass-ceramic materials. Appl. Surf. Sci. 363, 548–554 (2016).  https://doi.org/10.1016/j.apsusc.2015.12.079CrossRefGoogle Scholar
  3. 3.
    Zhang, Y., Xu, Z., Zhu, Y., Zhu, D.: Effect of tube-electrode inner structure on machining performance in tube-electrode high-speed electrochemical discharge drilling. J. Mater. Process. Technol. 231, 38–49 (2016)CrossRefGoogle Scholar
  4. 4.
    Alahmari, A., Darwish, S., Ahmed, N.: Laser beam micro-milling (LBMM) of selected aerospace alloys. Int. J. Adv. Manuf. Technol. 86, 2411–2431 (2016).  https://doi.org/10.1007/s00170-015-8318-1CrossRefGoogle Scholar
  5. 5.
    Elkaseer, A., Lambarri, J., Sarasua, J., Cascón, I.: On the development of a chip breaker in a metal-matrix polycrystalline diamond insert: finite element based design with ns-laser ablation and machining verification. J. Micro Nano-Manuf. 5(3), 031007–031007-12 (2017).  https://doi.org/10.1115/1.4036933
  6. 6.
    Li, T., Loua, Q., Dong, J., Wei, Y., Liu, J.: Modified surface morphology in surface ablation of cobalt-cemented tungsten carbide with pulsed UV laser radiation. Appl. Surf. Sci. 172, 331–344 (2001)CrossRefGoogle Scholar
  7. 7.
    Eberle, G., Wegener, K.: Ablation study of WC and PCD composites using 10 picosecond and 1 nanosecond pulse durations at green and infrared wavelengths. Phys. Procedia 56, 951–962 (2014)CrossRefGoogle Scholar
  8. 8.
    See, T., Chantzis, D., Royer, R., Metsios, I., Antar, M., Marimuthu, S.: A comparison of the DPSS UV laser ablation characteristic of 1024 and H10F WC-Co. Opt. Laser Technol. 92, 101–108 (2017)CrossRefGoogle Scholar
  9. 9.
    Knowles, M., Rutterford, G., Karnakis, D., Ferguson, A.: Micro-machining of metals, ceramics and polymers using nanosecond lasers. Int. J. Adv. Manuf. Technol. 33, 95–102 (2007)CrossRefGoogle Scholar
  10. 10.
    Sajti, C., Sattari, R., Chichkov, B., Barcikowski, S.: Ablation efficiency of α-Al2O3 in liquid phase and ambient air by nanosecond laser irradiation. Appl. Phys. A 100, 203–206 (2010)CrossRefGoogle Scholar
  11. 11.
    Zehetner, J., Kraus, S., Lucki, M., Vanko, G., Dzuba, J., Lalinsky, T.: Manufacturing of membranes by laser ablation in SiC, sapphire, glass and ceramic for GaN/ferroelectric thin film MEMS and pressure sensors. Microsyst. Technol. 22, 1883–1892 (2016)CrossRefGoogle Scholar
  12. 12.
    Burden, F., Winkler, D.: Bayesian regularization of neural networks. In: Livingstone, D.J. (ed.) Artificial Neural Networks. Methods in Molecular Biology™, vol. 458. Humana Press, New York (2008)Google Scholar
  13. 13.
    MathWorks website. https://www.mathworks.com/help/nnet/ref/trainbr.html. Accessed 17 Apr 2018
  14. 14.

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Ahmed Elkaseer
    • 1
    • 2
  • Jon Lambarri
    • 3
  • Iban Quintana
    • 3
  • Steffen Scholz
    • 1
  1. 1.Karlsruhe Institute of TechnologyKarlsruheGermany
  2. 2.Faculty of EngineeringPort Said UniversityPort SaidEgypt
  3. 3.IK4-TEKNIKEREibarSpain

Personalised recommendations