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Uncertainty Quantification in Metallic Additive Manufacturing Through Physics-Informed Data-Driven Modeling

  • Multiscale Computational Strategies for Heterogeneous Materials with Defects: Coupling Modeling with Experiments and Uncertainty Quantification
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Abstract

The complicated metal-based additive manufacturing (AM) process involves various sources of uncertainty, leading to variability in AM products. For comprehensive uncertainty quantification (UQ) of AM processes, we present a physics-informed data-driven modeling framework, in which multilevel data-driven surrogate models are constructed based on extensive computational data obtained by multiscale multiphysics AM models. It starts with computationally inexpensive surrogate models for which the uncertainty can be readily quantified, followed by global sensitivity analysis for comprehensive UQ study. Using AM-fabricated Ti-6Al-4V components as examples, this study demonstrates the capability of the proposed data-driven UQ framework for efficient investigation of uncertainty propagation from process parameters to material microstructures, then to macrolevel mechanical properties through a combination of advanced AM multiphysics simulations and data-driven surrogate modeling. Model correction and parameter calibration for the constructed surrogate models using limited amounts of experimental data are discussed.

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References

  1. W.E. Frazier, J. Mater. Eng. Perform. 23, 1917 (2014). https://doi.org/10.1007/s11665-014-0958-z.

    Article  Google Scholar 

  2. H. Bikas, P. Stavropoulos, and G. Chryssolouris, Int. J. Adv. Manuf. Technol. 83, 389 (2016).

    Article  Google Scholar 

  3. P. Nath, Z. Hu, and S. Mahadevan, Solid Freeform Fabrication (Austin, TX, USA, 2017), pp. 922–937.

  4. W. Yan, J. Smith, W. Ge, F. Lin, and W.K. Liu, Comput. Mech. 56, 265 (2015).

    Article  Google Scholar 

  5. A. Klassen, A. Bauereiß, and C. Körner, J. Phys. D Appl. Phys. 47, 065307 (2014).

    Article  Google Scholar 

  6. C. Körner, E. Attar, and O. Heinl, J. Mater. Process. Technol. 211, 978 (2011).

    Article  Google Scholar 

  7. C. Qiu, C. Panwisawas, M. Ward, H.C. Basoalto, J.W. Brooks, and M.M. Attallah, Acta Mater. 96, 72 (2015).

    Article  Google Scholar 

  8. L. Ma, J. Fong, B. Lane, S. Moylan, J. Filliben, A. Heckert, and L. Levine, Using design of experiments in finite element modeling to identify critical variables for laser powder bed fusion, in International Solid Freeform Fabrication Symposium, Laboratory for Freeform Fabrication and the University of Texas Austin, TX, USA (2015), pp. 219–228.

  9. Z. Hu and S. Mahadevan, Int. J. Adv. Manuf. Technol. 93, 2855 (2017).

    Article  Google Scholar 

  10. W. Chen, J.K. Allen, K.-L. Tsui, and F. Mistree, J. Mech. Des. 118, 478 (1996).

    Article  Google Scholar 

  11. Z. Wang, P. Liu, Y. Xiao, X. Cui, Z. Hu, and L. Chen, J. Manuf. Sci. Eng. (2019). https://doi.org/10.1115/1.4043798.

    Google Scholar 

  12. S. Chan and A.H. Elsheikh, J. Comput. Phys. 354, 493 (2018).

    Article  MathSciNet  Google Scholar 

  13. Y. Zhu and N. Zabaras, J. Comput. Phys. 366, 415 (2018).

    Article  MathSciNet  Google Scholar 

  14. C. Kamath, Int. J. Adv. Manuf. Technol. 86, 1659 (2016).

    Article  Google Scholar 

  15. F. Lopez, P. Witherell, and B. Lane, J. Mech. Des. 138, 114502 (2016).

    Article  Google Scholar 

  16. G. Tapia, W. King, L. Johnson, R. Arroyave, I. Karaman, and A. Elwany, J. Manuf. Sci. Eng. 140, 121006 (2018).

    Article  Google Scholar 

  17. P. Liu, X. Cui, J. Deng, S. Li, Z. Li, and L. Chen, Int. J. Therm. Sci. 136, 217 (2019).

    Article  Google Scholar 

  18. P. Liu, Z. Wang, Y. Xiao, M.F. Horstemeyer, X. Cui, and L. Chen, Addit. Manuf. 26, 22 (2019).

    Article  Google Scholar 

  19. R.A. Lebensohn, A.K. Kanjarla, and P. Eisenlohr, Int. J. Plast 32, 59 (2012).

    Article  Google Scholar 

  20. V. Tari, R.A. Lebensohn, R. Pokharel, T.J. Turner, P.A. Shade, J.V. Bernier, and A.D. Rollett, Acta Mater. 154, 273 (2018).

    Article  Google Scholar 

  21. S. Price, J. Lydon, K. Cooper, and K. Chou, Experimental temperature analysis of powder-based electron beam additive manufacturing, in Proceedings of the Solid Freeform Fabrication Symposium (2013), pp. 162–173.

  22. L.-Q. Chen, Annu. Rev. Mater. Res. 32, 113 (2002).

    Article  Google Scholar 

  23. X. Wang, P. Liu, Y. Ji, Y. Liu, M. Horstemeyer, and L. Chen, J. Mater. Eng. Perform. 28, 657 (2019). https://doi.org/10.1007/s11665-018-3620-3.

    Article  Google Scholar 

  24. P. Liu, Y. Ji, Z. Wang, C. Qiu, A. Antonysamy, L.-Q. Chen, X. Cui, and L. Chen, J. Mater. Process. Technol. 257, 191 (2018).

    Article  Google Scholar 

  25. J. Thomas, M. Groeber, and S. Ghosh, Mater. Sci. Eng., A 553, 164 (2012).

    Article  Google Scholar 

  26. Y. Ji, L. Chen, and L.-Q. Chen, Chapter 6: understanding microstructure evolution during additive manufacturing of metallic alloys using phase-field modeling.Thermo-Mechanical Modeling of Additive Manufacturing, ed. M. Gouge and P. Michaleris (Oxford: Butterworth-Heinemann, 2018), pp. 93–116.

    Chapter  Google Scholar 

  27. R. Bansal (B.Tech. thesis, National Institute of Technology-Rourkela, 2011).

  28. A. O’Hagan, SIAM/ASA J. Uncertain. Quantif. 20, 1 (2013).

    Google Scholar 

  29. G. Tapia, S. Khairallah, M. Matthews, W.E. King, and A. Elwany, Int. J. Adv. Manuf. Technol. 94, 3591 (2018).

    Article  Google Scholar 

  30. W. King, A. Anderson, R. Ferencz, N. Hodge, C. Kamath, S. Khairallah, and A. Rubenchik, Appl. Phys. Rev. 2, 041304 (2015).

    Article  Google Scholar 

  31. W. Zhang, A. Mehta, P.S. Desai, and C.F. Higgs III, Machine Learning Enabled Powder Spreading Process Map for Metal Additive Manufacturing (AM), 2017 Solid Freeform Fabrication Symposium Proceedings (2017).

  32. Z. Hu and S. Mahadevan, Struct. Multidiscip. Optim. 53, 501 (2016).

    Article  MathSciNet  Google Scholar 

  33. Z. Hu, D. Ao, and S. Mahadevan, Comput. Methods Appl. Mech. Eng. 318, 92 (2017).

    Article  Google Scholar 

  34. A. Chatterjee, Curr. Sci. 78, 808 (2000).

    Google Scholar 

  35. J.C. Helton and F.J. Davis, Reliab. Eng. Syst. Saf. 81, 23 (2003).

    Article  Google Scholar 

  36. M.C. Kennedy and A. O'Hagan, J. R. Stat. Soc. Ser. B. (Stat. Method) 63, 425 (2001).

    Article  Google Scholar 

  37. J. Brynjarsdóttir and A. O'Hagan, Inverse Probl. 30, 114007 (2014).

    Article  Google Scholar 

  38. D. Higdon, J. Gattiker, B. Williams, and M. Rightley, J. Am. Stat. Assoc. 103, 570 (2008).

    Article  Google Scholar 

  39. Z. Hu and S. Mahadevan, Scr. Mater. 135, 135 (2017).

    Article  Google Scholar 

  40. D. Moser, S. Fish, J. Beaman, and J. Murthy, Multi-Layer Computational Modeling of Selective Laser Sintering Processes, ASME 2014 International Mechanical Engineering Congress and Exposition (American Society of Mechanical Engineers), pp. V02AT02A008-V002AT002A008.

  41. M. Haines, A. Plotkowski, C.L. Frederick, E.J. Schwalbach, and S.S. Babu, Comput. Mater. Sci. 155, 340 (2018).

    Article  Google Scholar 

  42. A. Girard, C.E. Rasmussen, J.Q. Candela, and R. Murray-Smith, Advances in Neural Information Processing Systems (Vancouver, BC, Canada, 2003), pp. 545–552.

  43. S. Al-Bermani, M. Blackmore, W. Zhang, and I. Todd, Metall. Mater. Trans. A 41, 3422 (2010).

    Article  Google Scholar 

  44. B. Cheng, S. Price, J. Lydon, K. Cooper, and K. Chou, J. Manuf. Sci. Eng. 136, 061018 (2014).

    Article  Google Scholar 

  45. C.U. Brown, G. Jacob, M. Stoudt, S. Moylan, J. Slotwinski, and A. Donmez, J. Mater. Eng. Perform. 25, 3390 (2016).

    Article  Google Scholar 

  46. Z. Hu and S. Mahadevan, Reliab. Eng. Syst. Saf. 187, 40 (2019).

    Article  Google Scholar 

  47. M. Tang, P.C. Pistorius, and J.L. Beuth, Addit. Manuf. (2017).

  48. S.A. Khairallah, A.T. Anderson, A. Rubenchik, and W.E. King, Acta Mater. 108, 36 (2016).

    Article  Google Scholar 

  49. P. Prabhakar, W.J. Sames, R. Dehoff, and S.S. Babu, Addit. Manuf. 7, 83 (2015).

    Article  Google Scholar 

  50. X. Lu, X. Lin, M. Chiumenti, M. Cervera, Y. Hu, X. Ji, L. Ma, H. Yang, and W. Huang, Addit. Manuf. 26, 166 (2019).

    Article  Google Scholar 

  51. S.-I. Park, D.W. Rosen, S.K. Choi, and C.E. Duty, Addit. Manuf. 1, 12 (2014).

    Article  Google Scholar 

  52. W. Yan, Y. Lian, C. Yu, O.L. Kafka, Z. Liu, W.K. Liu, and G.J. Wagner, Comput. Methods Appl. Mech. Eng. 339, 184 (2018).

    Article  Google Scholar 

Download references

Acknowledgements

This research was supported by the program of ORAU Ralph E. Powe Junior Faculty Enhancement Award and National Science Foundation under Grant CMMI-1662854. The computer simulations were carried out on the clusters of High Performance Computing Collaboratory (HPC2) at Mississippi State University. We thank Dr. Ricardo A. Lebensohn for the EVP-FFT research code.

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Authors and Affiliations

  1. Department of Mechanical Engineering, Mississippi State University, Starkville, MS, 39762, USA

    Zhuo Wang, Pengwei Liu, Mark F. Horstemeyer & Lei Chen

  2. State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body, Hunan University, Changsha, 410082, People’s Republic of China

    Pengwei Liu

  3. Department of Materials Science and Engineering, The Pennsylvania State University, University Park, PA, 16802, USA

    Yanzhou Ji & Long-Qing Chen

  4. Department of Civil and Environmental Engineering, Vanderbilt University, Nashville, TN, 37235, USA

    Sankaran Mahadevan

  5. Department of Industrial and Manufacturing Systems Engineering, University of Michigan-Dearborn, Dearborn, MI, 48128, USA

    Zhen Hu

  6. Department of Mechanical Engineering, University of Michigan-Dearborn, Dearborn, MI, 48128, USA

    Lei Chen

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  1. Zhuo Wang
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  2. Pengwei Liu
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  8. Long-Qing Chen
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Correspondence to Zhen Hu or Lei Chen.

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Wang, Z., Liu, P., Ji, Y. et al. Uncertainty Quantification in Metallic Additive Manufacturing Through Physics-Informed Data-Driven Modeling. JOM 71, 2625–2634 (2019). https://doi.org/10.1007/s11837-019-03555-z

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