An ICME Framework for Incorporating Bulk Residual Stresses in Rotor Component Design
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Integrated Computational Materials Engineering (ICME) is an emerging discipline that aims to integrate computational materials science tools into a holistic system that can accelerate materials development, transform the engineering design optimization process, and unify design and manufacturing. A team of aerospace Original Equipment Manufacturers (OEMs) and suppliers have executed a critical program to address the United States Air Force (USAF) funded Foundational Engineering Problem (FEP) on residual stress within nickel-base superalloy components. This program was aimed at establishing methods to link predictive tools to component design functions and product realization activities with industry-wide standardized protocols. The multi-disciplinary approach links supplier and OEM materials and process models with structural analysis tools to enable manufacturing parameter selection based on disk design criteria. By linking analytical tools between the supplier and OEM, process parameters may be optimized for reduced scrap, while optimizing disk designs for design requirements. A significant challenge to doing this is qualifying and integrating sources of variation in the materials and process models with design and structural analysis tools. This paper reviews ICME infrastructure tools and methods that were used to demonstrate and validate linked residual stress-based materials and manufacturing model capabilities with design activities to achieve an optimized final component. This work was funded by the United States Air Force through the Metals Affordability Initiative (MAI).
KeywordsICME FEP Model-based materials definitions Residual stress IN718 Disk growth
This work was conducted under the USAF’s Foundational Engineering Problem (FEP) on bulk residual stress development in nickel-base superalloys under the auspices of the Metals Affordability Initiative, Contract No. FA8650-13-2-5201. The support and encouragement of the FEP Program Managers (B. Song, T.J. Turner, and M.J. Caton) are gratefully acknowledged. The authors also thank Dr. Lee Semiatin at AFRL and Mr. Adrian DeWald at Hill Engineering for their excellent technical support.
- 1.Committee On Integrated Computational Engineering (2008) Integrated computational materials engineering: a transformational discipline for improved competitiveness and national security. National Academies Press, Washington, DCGoogle Scholar
- 2.The White House (2011) The materials genome initiative. http://www.whitehouse.gov/mgi Accessed 13 May 2014
- 5.Pollock T et al (2008) Integrated Computational Materials Engineering: a transformational discipline for improved competitiveness and national security, vol 7. the National Academies Press, Washington DCGoogle Scholar
- 6.Wong T, Venkatesh V, Turner TJ (2015) Data Infrastructure Developed for PW-8: Nickel Base Superalloy Residual Stress Foundational Engineering Problem. In: Poole W. et al. (eds) Proceedings of the 3rd World Congress on Integrated Computational Materials Engineering (ICME 2015). (Pittsburgh, PA: The Minerals, Metals & Materials Society; New York: Springer, 2015), pp. 247–259. https://doi.org/10.1007/978-3-319-48170-8_30
- 7.Ward C, Warren J, Hanisch R (2014) Making materials science and engineering data more valuable research products. Integr Mater Manuf Innov 3(1):22Google Scholar
- 8.Cernatescu I, Venkatesh V, Glanovsky JL, Landry LH, Green RN, Gynther D, Furrer DU, Turner TJ (2015) Residual-stress-measurement implementation in model-validation process as applied in the USAF Foundational-Engineering-Problem program on ICME of bulk residual stress in Ni Rotors,” 56th AIAA/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference, AIAA SciTech Forum, paper AIAA–0387Google Scholar
- 9.Rolph J, Preuss M, Iqbal N, Hofmann M, Nikov S, Hardy MC, Glavicic MG, Ramanathan R, Evans A (2012) Residual stress evolution during the manufacture of aerospace forgings, Proceedings of the 12th International Symposium on Superalloys, edited by E. Huron, R. Reed, M. Hardy, M. Mills, R. Montero, P. Portella, J. Telesman, TMS. (Warrendale, PA: TMS, 2012), pp. 881-891Google Scholar
- 11.Chan K, Saltelli A, Tarantola S (1997) Sensitivity analysis of model output: variance-based methods make the difference. In: Proceedings of the 29th Winter Simulation Conference, edited by; S. Andradottir, K. Healy, D. Withers, B. Nelson, IEEE Computer Society Washington, DC, USA.Google Scholar
- 13.Mackay DJ (1998) Introduction to Gaussian Processes. NATO ASI Series - Neural Networks and Machine Learning, Bishop (Ed.). NATO ASI Series. 168:133–165Google Scholar
- 14.O'Hagan T (2009) GEM-SA. Retrieved September 14, 2016, from The GEM Software Project: http://www.tonyohagan.co.uk/academic/GEM/
- 16.Reinman G, Ayer T, Davan T, Devore M, Finley S, Glanovsky J, Gray L, Hall B, Jones C, Learned A, Mesaros E, Morris R, Pinero S, Russo R, Stearns E, Teicholz M, Teslik-Welz W, Yudichak D (2012) Design for variation. Quality Engg: Special Issue: Statistical Eng. 24(2):317–345. https://doi.org/10.1080/08982112.2012.651973