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