Advertisement

Towards rapid qualification of powder-bed laser additively manufactured parts

  • A. D. PeraltaEmail author
  • M. Enright
  • M. Megahed
  • J. Gong
  • M. Roybal
  • J. Craig
Research
Part of the following topical collections:
  1. Enabling Additive Manufacturing through Digital-Data and Model Integration

Abstract

Qualification of aerospace components is a long and costly process involving material properties, material specifications, manufacturing process, and design among others. Reducing qualification time and cost while maintaining safety offers a large economic advantage and enables faster response to the market demands. In 2012, DARPA established the Open Manufacturing program, a project to develop an integrated computational materials engineering (ICME) framework aimed at rapid qualification. Rapid qualification requires the integration of several technologies: materials, process, design, models, monitoring and control, non-destructive evaluation (NDE), testing, among others. A probabilistic design approach is adopted in the rapid qualification process to enable the integration of these technologies into a single risk-based function to optimize the design process. This approach directs the efforts to those areas that play the most important roles, potentially reducing specimen testing that will be required to develop material databases and design limits. New tests also will be required to validate and verify the ICME framework and develop a better understanding of the processing-microstructure-property relation and associated variability of the processing conditions. The probabilistic design approach is demonstrated for the rapid qualification of an actual aircraft engine component constructed via the powder-bed additive manufacturing process. This paper summarizes the probabilistic rapid qualification design approach and its application to this novel manufacturing process with the goal of reducing the overall qualification process time by 40 % and qualification process cost by 20 %.

Keywords

Rapid qualification Integrated computational materials engineering Uncertainty quantification Process modeling Materials modeling Process monitoring Non-destructive evaluation Verification and validation 

Notes

Acknowledgements

This work was performed under the DARPA Open Manufacturing Program entitled “Rapid Low Cost Additive Manufacturing” contract number HR001-12-C-0037 to Honeywell International Inc. The authors acknowledge the financial support and the guidance of the managing panel.

We would also like to acknowledge our colleagues who have helped with different aspects of the program. From Honeywell: J. Neumann, H. Deutchman, B. Baughman, P. Kantzos, M. Kemp, S. Singh, B. Shula., and G. Levesque. From SwRI: J. McFarland. From ESI: N. N’Dri, H.-W. Mindt. From Questek: D. Snyder, G. Olson, J. Sebastien. From Sigma Labs: M. Cola. V. Dave. From Stratonics: T. Wakeman.

Funding

This work presented in the manuscript was performed under the DARPA Open Manufacturing Program entitled “Rapid Low Cost Additive Manufacturing” contract number HR001-12-C-0037 to Honeywell International Inc. The manuscript itself was prepared using DARPA funding.

References

  1. 1.
  2. 2.
    Haugen EB, Wirsching P (1975) Probabilistic design. Machine Design. April 17 pp. 98–104Google Scholar
  3. 3.
    Gorelik M, Obayami J, Slovisky J, Frias D, Swanson H, McFarland J, Enright M, Riha D (2013) Effect of manufacturing variability on turbine engine performance: a probabilistic study. s.n, San Antonio, TX, pp GT2013–95145, Proceedings of ASME Turbo ExpoGoogle Scholar
  4. 4.
    Enright MP, McClung JRC, Moody JP, McFarland JM, Wu WT, Shankar R (2013) Probabilistic integration of material mrocess modeling and fracture risk assessment using Gaussian Process models. s.n, Boston, MA, Proceedings of the 54th AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials ConferenceGoogle Scholar
  5. 5.
    McFarland JM (2015) Variance decomposition for statistical quantities of interest. Journal of Aerospace Information Systems 12:204–218, Special Section on Uncertainty QuantificationCrossRefGoogle Scholar
  6. 6.
    ESI Group (2014) CFD-ACE+ Theory and User Manuals. s.l. : ESI GroupGoogle Scholar
  7. 7.
    N’Dri N, Mindt H-W, Shula B, Megahed M, Peralta A, Kantzos P, Neumann J (2015) DMLS process modelling & validation. Wiley, TMS 2015 144th Annual Meeting & Exhibition, Orlando, 978-1-119-08241-5Google Scholar
  8. 8.
    Megahed M, Mindt H-W, N’Dri N, Duan H-Z, Desmaison O (2016) Metal additive manufacturing process and residual stress modelling. s.l. : Springer Verlag, IMMI. Integrating Materials and Manufacturing Innovation, 5:4. doi: https://doi.org/10.1186/s40192-016-0047-2
  9. 9.
    DMLM models—numerical assessment of porosity. H.-W. Mindt, M. Megahed, A.D. Peralta, J. Neumann. Phoenix, AZ., USA. : s.n., 2015. 22nd ISABE Conference, Oct. 25–30Google Scholar
  10. 10.
    ESI Group (2015) SYSWeld Theory and User Manuals. s.l. : ESI GroupGoogle Scholar
  11. 11.
    Vogel M, Khan M, Ibarra-Medina J, Pinkerton AJ, N'Dri N, and Megahed M (2013) A coupled approach to weld pool, phase and residual stress modelling of Laser Direct Metal Deposition (LDMD) Processes. Salt Lake City : s.n.,. 978-1-11876-689-7. 2 World Congress on Integrated Computational Materials Engineering, 12 Jul 2013Google Scholar
  12. 12.
    Kuehmann CJ, Olson GB (2009) Computational materials design and engineering. 4, Materials Science and Technology, Vol. 25Google Scholar
  13. 13.
  14. 14.
    Dave VR, Hartman DA, Cola MJ (2009) In-Process Quality Assurance for aerospace welding applications. s.l. : American Welding Society, Welding Journal, 88:28–32Google Scholar
  15. 15.
    Dave VR, Hartman DA, King WH, Cola MJ, Vaidya RU (2003) Strategy for small lot manufacturing: In-Process Monitoring and Control. Los Alamos National Laboratory. LA Science, Los Alamos, NM, pp 63–67, 28Google Scholar
  16. 16.
    Marshall GJ, Young WJ II, Craig J, Wakeman T, Thompson SM (2015) Dual thermographic monitoring of Ti-6Al-4V cylinders using direct laser deposition. Austin, TX : s.n., 26th International Solid Freeform Fabrication Symposium - An Additive Manufacturing ConferenceGoogle Scholar
  17. 17.
    Cowles B, Backman D, Dutton R (2012) Verification and validation of ICME methods and models for aerospace application. 2, s.l. : SpringerOpen, Integrating Materials and Manufacturing Innovations, Vol. 1, 11 June 2012Google Scholar

Copyright information

© Peralta et al. 2016

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (https://doi.org/creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Authors and Affiliations

  • A. D. Peralta
    • 1
    Email author
  • M. Enright
    • 2
  • M. Megahed
    • 3
  • J. Gong
    • 4
  • M. Roybal
    • 5
  • J. Craig
    • 6
  1. 1.Honeywell AerospacePhoenixUSA
  2. 2.Southwest Research InstituteSan AntonioUSA
  3. 3.ESI NAColumbiaUSA
  4. 4.QuesTek Innovations, LLCEvanstonUSA
  5. 5.Sigma LabsSanta FeUSA
  6. 6.Stratonics, Inc.Lake ForestUSA

Personalised recommendations