The aerospace industry is now beginning to adopt Additive Manufacturing (AM), both for new aircraft design and to help improve aircraft availability (aircraft sustainment). However, MIL-STD 1530 highlights that to certify airworthiness, the operational life of the airframe must be determined by a damage tolerance analysis. MIL-STD 1530 also states that in this process, the role of testing is merely to validate or correct the analysis. Consequently, if AM-produced parts are to be used as load-carrying members, it is important that the $da/dN$ versus ΔK curves be determined and, if possible, a valid mathematical representation determined. The present paper demonstrates that for AM Ti-6Al-4V, AM 316L stainless steel, and AM AerMet 100 steel, the $da/dN$ versus ΔK curves can be represented reasonably well by the Hartman-Schijve variant of the NASGRO crack growth equation. It is also shown that the variability in the various AM $da/dN$ versus $\Delta K$ curves is captured reasonably well by using the curve determined for conventionally manufactured materials and allowing for changes in the threshold and the cyclic fracture toughness terms.

A digital twin (DT), with the components of a physics-based model, a data-driven model, and a machine learning (ML) enabled efficient surrogate model, behaves as a virtual twin of the real-world physical process. In terms of Laser Powder Bed Fusion (L-PBF) based additive manufacturing (AM), a DT can predict the current and future states of the melt pool and the resulting defects corresponding to the input laser parameters, evolve itself by assimilating in-situ sensor data, and optimize the laser parameters to mitigate defect formation. In this paper, we present a deep neural operator enabled DT framework for closed-loop feedback control of the L-PBF process. This is accomplished by building a physics-based computational model to accurately represent the melt pool states, an efficient Fourier neural operator (FNO) based surrogate model to approximate the melt pool solution field, followed by a physics-based procedure to extract information from the computed melt pool simulation that can further be correlated to the defect quantities of interest (e.g., surface roughness). An optimization algorithm is then exercised to control laser input and minimize defects. On the other hand, the constructed DT also evolves with the physical twin via offline finetuning and online material calibration. For instance, the probabilistic distribution of laser absorptivity can be updated to match the real-time captured thermal image data. Finally, a probabilistic framework is adopted for uncertainty quantification. The developed DT is envisioned to guide the AM process and facilitate high-quality manufacturing in L-PBF-based metal AM.

The efficacy of hot isostatic pressing (HIP) for enhancing fatigue performance is investigated for additively manufactured (AM) Ti-6Al-4V. The limitations of HIP are probed by varying the initial material state via the selection of AM system, powder chemical composition, and process parameters. We demonstrate that the fatigue performance of HIP’d AM Ti-6Al-4V depends on the as-built quality of the material. Differences in common material attributes, such as pre-HIP defect populations or post-HIP microstructure morphology, are shown to be insufficient to explain the observed discrepancies in performance. This implies that additional microstructure attributes or localized deviations from the expected structure control the failure of this material. Finally, HIP parameters outside ASTM recommendations were explored, where a reduced temperature and high-pressure treatment yielded significantly improved fatigue performance.

Understanding and characterizing crack growth is central to meeting the damage tolerance and durability requirements delineated in USAF Structures Bulletin EZ-SB-19-01 for the utilization of additive manufacturing (AM) in the sustainment of aging aircraft. In this context, the present paper discusses the effect of different AM processes, different build directions, and the variability in the crack growth rates related to AM Ti-6Al-4V, AM Inconel 625, and AM 17-4 PH stainless steel. This study reveals that crack growth in these three AM materials can be captured using the Hartman–Schijve crack growth equation and that the variability in the various da/dN versus ΔK curves can be modeled by allowing the terms ΔK_thr and A to vary. It is also shown that for the AM Ti-6AL-4V processes considered, the variability in the cyclic fracture toughness appears to be greatest for specimens manufactured using selective layer melting (SLM).