Fatigue Prediction for Additive Manufactured (AM) Metallic Components
Navy STTR 2019.B - Topic N19B-T026
NAVAIR - Ms. Donna Attick - email@example.com
Opens: May 31, 2019 - Closes: July 1, 2019 (8:00 PM ET)
TECHNOLOGY AREA(S): Air Platform, Ground/Sea Vehicles, Materials/Processes
OBJECTIVE: Develop a comprehensive toolset to predict the fatigue life of flight-critical metallic components fabricated by additive manufacturing.
DESCRIPTION: Additive manufacturing (AM) is considered a revolutionary technology to fabricate lightweight, flight- and marine-critical metallic components. The ability to produce complex and tailored structure designs opens the door for improved efficiency in existing products and can function as a key enabler to new uses like hypersonic applications. Many merits, such as high efficiency, flexibility, and cost saving, give AM the potential to become a widely utilized fabrication process for industrial applications. Despite the high potential of this manufacturing technology, it has been found that the fatigue life of as-deposited AM components is often low compared to wrought components produced by conventional technology. For critical components, like those in airframe applications, developing a better understanding of fatigue performance is essential for further adoption of this technology [Ref 1].
AM is far more complex than traditional fabrication processes. The starting material is typically a high-quality powder with specific characteristics such as size, morphology, and chemical composition. The AM process is comprised of numerous cycles of material addition and rapid heating and cooling / melting and solidifying. As a result, the fatigue performance in AM parts has been attributed to a complex combination of material and process- induced imperfections. For example, fatigue crack growth mechanisms have been correlated with microstructure, such as a/a’ phases and colonies, in AM-fabricated Ti-6Al-4V [Ref 2]. Fatigue performance has also been found to be strongly related to porosity and defects that could be formed due to localized incomplete melting, often influenced by process parameters [Ref 3]. As with traditional machine design rules, the fatigue lives of an AM part are dominated by surface roughness effects. The effect of residual stress on fatigue performance has also been demonstrated by removing a compressive residual stressed surface layer to reduce fatigue performance [Ref 4].
Due to the complexity of fatigue behavior of an AM part, a comprehensive toolset, based on an Integrated Computational Materials Engineering (ICME) framework [Ref 5], is needed to predict fatigue strength and fatigue life in AM metallic components. This toolset should address the fatigue contributing factors at the part level, such as residual stress during the AM process, the microstructure of the fabricated metallic component, porosity level and distribution in the AM part, and surface roughness. This toolset should be able to assess fatigue environments typically experienced by Navy aircraft like flight spectra [Ref 6] and shocks and vibration [Ref 7]. Similar to the integrative approach in foundry processes (castings) [Ref 8], the AM fatigue predictive methodology may integrate a combination of AM process simulations to predict AM anomalies, crack growth modeling to predict the effect of the AM anomalies on fatigue life and residual strength, and modeling of nondestructive evaluation (NDE) processes to determine the inspectability of both initial anomalies and potential cracks that may grow while the component is in service. Artificial intelligence strategies like machine learning and neural networks may be integrated into the toolset. This toolset should be compatible with existing analysis software toolsets (e.g., FE-SAFE [Ref 9], nCode [Ref 10], AFGROW [Ref 11], NASGRO [Ref 12]) and exhibit equal or better performance and accuracy.
Component size limitations are largely driven by the build volume of the AM machine being used. As the technology continues to evolve, so will the build volume. For purposes of this effort, components between 2”x2”x2” and 15”x15”x15” are acceptable, however the long-term goal is for larger capability.
PHASE I: Demonstrate the feasibility of a predictive methodology for fatigue properties of metallic AM components (relating the material and processing induced imperfections noted above.) Show the feasibility by performing limited predictions of the fatigue performance of a single material (e.g., Ti-6Al-4V or 17-4PH) for a single AM machine. Validate the predicted fatigue behavior of the deposited material and characterize at a coupon
level. Identify the issues involved in integrating the fatigue predictive methodology. Include, in a Phase II plan, full- scale methodology development plans to be carried out under Phase II.
PHASE II: Further develop the predictive toolset so that it can be applicable to an array of aircraft component geometries and materials, and useable across multiple machines (e.g., one powder bed machine and one powder blown machine.). Demonstrate the predictive tool on an article that is representative of basic geometries seen on aircraft components (e.g., overhangs, holes, fillets/radii, internal channels, lugs). Perform analysis of the predictive methodology to determine its ability to predict fatigue behavior of AM parts. Fully validate the predictive fatigue lives of the AM parts.
PHASE III DUAL USE APPLICATIONS: Fully develop the predictive fatigue toolset and demonstrate it in a scenario representative of Navy implementation (i.e., using similar equipment, skillsets, and selected part(s) that would be available in a Navy application). Transition the prediction tool into a standalone and/or combined product for use in Navy and commercial additive manufacturing applications. Ensure that the software tool developed through this effort will enable designers and manufacturers to better identify and address features, characteristics, and potential anomalies that could negatively impact fatigue life prior to part production, which will help to improve the quality of additively manufactured parts as well as increase the efficiency of the AM process by reducing the number of builds that fail to meet performance requirements. As these aspects are valuable to all types of AM, this toolset will be directly applicable to a wide range of commercial applications (e.g., aerospace, marine, automotive, and oil and gas.)
1. Li, P., Warner, D., Fatemi, A., and Phan, N. "Critical assessment of the fatigue performance of additively manufactured Ti–6Al–4V and perspective for future research." International Journal of Fatigue, Volume 85, April 2016, pp. 130-143. https://doi.org/10.1016/j.ijfatigue.2015.12.003
2. Zhai, Y., Galarraga, H., and Lados, D. A. "Microstructure, static properties, and fatigue crack growth mechanisms in Ti-6Al-4V fabricated by additive manufacturing: LENS and EBM." Engineering Failure Analysis, Volume 69, 2016, pp. 3-14. https://doi.org/10.1016/j.engfailanal.2016.05.036
3. Hrabe, N., Gnäupel-Herold, T., and Quinn, T. "Fatigue properties of a titanium alloy (Ti–6Al–4V) fabricated via electron beam melting (EBM): Effects of internal defects and residual stress." International Journal of Fatigue, Volume 94, 2017, pp. 202-210. https://doi.org/10.1016/j.ijfatigue.2016.04.022
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5. "Integrated Computational Materials Engineering: A Transformational Discipline for Improved Competitiveness and National Security." National Research Council, 2008. https://doi.org/10.17226/12199
6. Heuler, P. and Klatschke, H. “Generation and use of standardized load spectra and load-time histories.” Int. J Fatigue, 2005, Volume 27, pp. 947-990. https://doi.org/10.1016/j.ijfatigue.2004.09.012
7. “Department of Defense Test Method Standard, Environmental Engineering Considerations and Laboratory Tests,” Revision G.” Military Standard (MIL-STD-810G), 31 October 2008. http://everyspec.com/MIL-STD/MIL- STD-0800-0899/MIL-STD-810G_12306/
8. Bordas, S. P., Conley, J. G., Moran, B., Gray, J., and Nichols, E. "A simulation-based design paradigm for complex cast components." Engineering with Computers, March 2007, Volume 23, Issue 1,, pp. 25-37. https://doi.org/10.1007/s00366-006-0030-1
9. "FE-SAFE - Durability Analysis Software for Finite Element Models." Dassault Systemes, 2018. https://www.3ds.com/products-services/simulia/products/fe-safe/fe-safe/
10. “nCode DesignLife.” HBM Prenscia Inc., 2018. https://www.ncode.com/products/designlife-cae-fatigue-analysis
11. “AFGROW (Air Force Growth) Fracture Mechanics and Fatigue Crack Growth Analysis Software.” LexTech, Inc., 2015. https://www.afgrow.net/
12. “NASGRO Fracture Mechanics & Fatigue Crack Growth Software.” Southwest Research Institute, 2018. https://www.swri.org/consortia/nasgro
13. Fieres J., Schumann, P., and Reinhart, C. “Predicting failure in additively manufactured parts using X-ray computed tomography and simulation.” Procedia Engineering, Volume 213, 2018, pp. 69-78. https://doi.org/10.1016/j.proeng.2018.02.008
14. Yadollahia A., Shamsaeia N., Thompsona S.M., Elwanyb A., Biana L., Mahmoudib M., “Fatigue behavior of selective laser melted 17-4 PH stainless steel.” 26th International Solid Freeform Fabrication Symposium - An Additive Manufacturing Conference, At Austin, TX, pp. 721. https://doi.org/10.13140/RG.2.1.3996.2323
KEYWORDS: Metal Additive Manufacturing; Fatigue Property Prediction; Process Modeling; Crack Growth; Non- destructive Evaluation; ICME