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Laser Additive Manufacturing- Direct Energy Deposition of Ti-15Mo Biomedical Alloy: Artificial Neural Network Based Modeling of Track Dilution

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Abstract

Direct energy deposition is one of the most popular laser additive manufacturing processes. However, the generation of geometry (or dilution and the resulting porosity) of the deposited tracks is not well understood as it is largely dependent on the laser process settings. In this work, the track dilution generated in single track deposition of Ti-15Mo alloy using the laser additive manufacturing – direct energy deposition (LAM-DED) process is accurately modeled and predicted, using the Matlab software based artificial neural network (ANN). Targeting a mean square error of zero, a 3–7-1 feed-forward, back-propagation, NN architecture with Levenberg-Marquardt training algorithm is developed. The percentage prediction error is calculated to validate the developed ANN model and is found to range between −0.981 and 0.584 establishing the goodness of the fitted model and the high prediction accuracy of track dilution. Further, regression analysis is also conducted for theoretical validation of the experimental track dilution. The correlation coefficient value obtained for 27 data is 0.982 which establishes high confidence in the fitted data. This study helps in accurate software based prediction of track dilution at different LAM-DED process parameter settings for possible fabrication of superior quality (low porosity) Ti-15Mo bio-implants.

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References

  1. Bhardwaj, T., Shukla, M., Paul, C.P., Bindra, K.S.: Direct energy deposition: laser additive manufacturing of titanium-molybdenum alloy - parametric studies, microstructure and mechanical properties. J. Alloys Compd. 787, 1238–1248 (2019)

    Article  Google Scholar 

  2. Geetha, M., Singh, A.K., Asokamani, R., Gogia, A.K.: Ti based biomaterials, the ultimate choice for orthopaedic implants: a review. Prog. Mater. Sci. 54(3), 397–425 (2009)

    Article  Google Scholar 

  3. Niinomi, M.: Mechanical properties of biomedical titanium alloys. Mat. Sci. Eng. A. 243(1-2), 231–236 (1998)

    Article  Google Scholar 

  4. Urena, J., Tsipas, S., Jimenez-Morales, A., Gordo, E., Detsch, R., Boccaccini, A.R.: In-vitro study of the bioactivity and cytotoxicity response of Ti surfaces modified by Nb and Mo diffusion treatments. Surf. Coat. Technol. 335, 148–158 (2018)

    Article  Google Scholar 

  5. Sumitomo, N., Noritake, K., Hattori, T., Morikawa, K., Niwa, S., Sato, K., Niinomi, M.: Experiment study on fracture fixation with low rigidity titanium alloy. J. Mater. Sci. Mater. Med. 19(4), 1581–1586 (2008)

    Article  Google Scholar 

  6. Zhou, Y.L., Luo, D.M.: Effects of ta content on Young's modulus and tensile properties of binary Ti-Ta alloys for biomedical applications Mater. Char., 62(10), 931–937 (2011)

  7. Oliveira, N.T.C., Guastaldi, A.C.: Electrochemical stability and corrosion resistance of Ti–Mo alloys for biomedical applications. Acta Biomater. 51, 399–405 (2009)

    Article  Google Scholar 

  8. Cardoso, F.F., Ferrandini, P.L., Lopes, E.S.N., Cremasco, A., Caram, R.: Ti-Mo alloys employed as biomaterials: effects of composition and aging heat treatment on microstructure and mechanical behavior. J. Mech. Behav. Biomed. Mater. 32, 31–38 (2014)

    Article  Google Scholar 

  9. Bhardwaj, T., Shukla, M., Prasad, N.K., Paul, C.P., Bindra, K.S.: Direct laser deposition - additive manufacturing of Ti-15Mo alloy: effect of build orientation induced surface topography on corrosion and bioactivity. Met. Mater. Int. (2019). https://doi.org/10.1007/s12540-019-00464-3

  10. Dilip, J.J.S., Zhang, S., Teng, C., Zeng, K., Robinson, C., Pal, D., Stuker, B.: Influence of processing parameters on the evolution of melt pool, porosity, and microstructures in Ti-6Al-4V alloy parts fabricated by selective laser melting. Prog. Addit. Manuf. 2(3), 157–167 (2017)

  11. Ning, J., Sievers, D.E., Garmestani, H., Liang, S.Y.: Analytical thermal modeling of metal additive manufacturing by heat sink solution. Materials. 12(16), 2568 (2019a)

    Article  Google Scholar 

  12. Ning, J., Sievers, D.E., Garmestani, H., Liang, S.Y.: Analytical modeling of in-process temperature in powder bed additive manufacturing considering laser power absorption, latent heat, scanning strategy, and powder packing. Materials. 12(5), 808 (2019b)

    Article  Google Scholar 

  13. Ning, J., Sievers, D.E., Garmestani, H., Liang, S.Y.: Analytical modeling of transient temperature in powder feed metal additive manufacturing during heating and cooling stages. Appl. Phys. A Mater. Sci. Process. 125, 1–11 (2019c)

    Article  Google Scholar 

  14. Ning, J., Mirkoohi, E., Dong, Y., Sievers, D.E., Garmestani, H., Liang, S.Y.: Analytical modeling of 3D temperature distribution in selective laser melting of Ti-6Al-4V considering part boundary conditions. J. Manuf. Process. 44, 319–326 (2019d)

    Article  Google Scholar 

  15. Mahapatra, M.M., Li, L.: Prediction of pulsed-laser powder deposits’ shape profiles using a back-propagation artificial neural network. Proc. Inst. Mech. Eng. B J. Eng. Manuf. 222, 1567–1576 (2008)

  16. Shukla, M., Tambe, P.B.: Predictive modelling of surface roughness and kerf widths in abrasive water jet cutting of Kevlar composites using neural network. Int. J. Mach. Mach. Mater. 8(1/2), 226–246 (2010)

    Google Scholar 

  17. Ganesh, P., Kaul, R., Paul, C.P., Tiwari, P., Rai, S.K., Prasad, R.C., Kukreja, L.M.: Fatigue and fracture toughness characteristics of laser rapid manufactured Inconel 625 structures. Mater. Sci. Eng. A. 527, 7490–7497 (2010)

    Article  Google Scholar 

  18. Hojjatzadeh, S.M.H., Parab, N.D., Yan, W., Guo, Q., Xiong, L., Zhao, C., Qu, M., Escano, L.I., Xiao, X., Fezzaa, K., Everhart, W., Sun, T., Chen, L.: Pore elimination mechanisms during 3D printing of metals. Nat. Commun. 10(1), 3088 (2019)

    Article  Google Scholar 

  19. Yi, H., Qi, L., Luo, J., Li, N.: Hole-defects in soluble core assisted aluminum droplet printing: metallurgical mechanisms and elimination methods. Appl. Therm. Eng. 148, 1183–1193 (2019)

    Article  Google Scholar 

  20. Yi, H., Qi, L., Luo, J., Zhang, D., Li, H., Hou, X.: Effect of the surface morphology of solidified droplet on remelting between neighboring aluminum droplets. Int. J. Mach. Tools Manuf. 130-131, 1–11 (2018)

    Article  Google Scholar 

  21. Yi, H., Qi, L., Luo, J., Jiang, Y., Deng, W.: Pinhole formation from liquid metal microdroplets impact on solid surfaces. Appl. Phys. Lett. 108(4), 041601 (2016)

    Article  Google Scholar 

  22. Yi, H., Qi, L., Luo, J., Zhang, D., Li, N.: Direct fabrication of metal tubes with high-quality inner surfaces via droplet deposition over soluble cores. J. Mater. Process. Technol. 264, 145–154 (2019)

    Article  Google Scholar 

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Acknowledgements

Thanks are largely due to Dr. C.P. Paul at Laser Additive Manufacturing Lab, Laser Technology Division, Raja Ramanna Center for Advance Technology Indore, India for granting access to the LAM-DED experimental setup. The lead author acknowledges the support of Ministry of Human Resource Development, Government of India for financial assistance as research fellowship.

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Authors and Affiliations

  1. Department of Mechanical Engineering, Motilal Nehru National Institute of Technology Allahabad, Prayagraj, India

    Tarun Bhardwaj & Mukul Shukla

  2. Department of Mechanical Engineering, Ajay Kumar Garg Engineering College, Ghaziabad, India

    Tarun Bhardwaj

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  1. Tarun Bhardwaj
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  2. Mukul Shukla
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Correspondence to Mukul Shukla.

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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, 245–258 (2020). https://doi.org/10.1007/s40516-020-00117-z

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