ABSTRACT

NON-TECHNICAL SUMMARY:

This project studies the crack tip plasticity in novel refractory complex concentrated alloys (RCCAs) to facilitate their applications in high-temperature applications. For example, hypersonics, safer nuclear fusion energy and fuel-efficient airplanes all require the use of metallic alloys that can retain high mechanical strength at extremely high temperatures (>> 1200 ?). Alloys based on refractory metals (such as Niobium (Nb), Molybdenum (Mo), Tantalum (Ta), Tungsten (W), and Rhenium (W), all of which have melting temperatures higher than 2000 ?) provide such possibilities. Typical alloys rely on a single refractory metal as its major (> 50%) chemical component. These however do not meet the criteria for high-temperature structural applications because their strengths are dramatically reduced as temperatures rise. Complex concentrated alloys (CCAs), involve high concentrations of multiple refractory elements and may meet extremely strict specifications. Refractory CCAs (RCCAs), such as NbMoTaW, may sustain high strengths across a wide range of high temperatures due to unique interactions between multiple chemical elements and the deformation defects inside these alloys. However, these RCCAs are usually brittle at room temperatures, making it difficult bend, form and shape these metals into forms suitable for use.

This project is generating new understanding of how to control mechanical deformation at the crack tip of RCCAs at room temperature by applying an integrated computational, experimental, and statistical method. The main goal is to activate various forms of plastic deformation in order to blunt the crack tips and slow fast crack propagations. Computer simulations based on quantum mechanics and statistical methods are being used to screen for possible RCCA compositions. Laser-directed energy deposition is also being carried out for fast production of the candidate compositions. Advanced mechanical testing and structural characterization tools at nanoscales are also being conducted to analyze deformation at crack tips. Finally, machine learning techniques are being applied to analyze the experimental testing and characterization results for the purpose of identifying novel alloy chemistries and the mechanisms needed to slow down crack propagations. This research project is enabling the implementation of RCCAs with high room-temperature ductility and formability, favorably impacting energy sustainability, aviation/aerospace, and other critical areas that require structural materials under extreme thermomechanical environments. Relatedly, the teaching and training elements of this project are: 1) enabling integrated-computational-materials-engineering (ICME) approaches and artificial intelligence (AI) concepts to be widely shared with senior undergraduate and graduate students, 2) championing outreach activities for students in K-12 students as well as 3) supporting opportunities for students of underrepresented groups to engage in state-of-the-art engineered materials research.

TECHNICAL SUMMARY:

The novel materials known as single-phase body-centered cubic (BCC) refractory complex concentrated alloys (RCCAs), which contain multiple refractory elements in high concentrations, exhibit high yield strengths at temperatures above the melting point of Ni-base superalloys. However, RCCAs lack room-temperature ductility, resulting in premature failure during manufacturing and mechanical loading. The challenges to enhance their room-temperature ductility to a large extent originate from the lack of understanding of their intrinsic ductility determined by deformation mechanisms at their crack tips. The commonly employed Rice criterion based on the classical Rice-Thomson model uses the energetics of a specific slip system and a particular cleavage plane orientation to predict the general trends of the ductile versus brittle crack tip behavior. However, chemical and stress complexity at crack tips in RCCAs may activate multiple deformation modes, but there is still a knowledge gap on whether and how the synergistic effects of multiple deformation modes on crack tip plasticity emerge in RCCAs to enhance their ductility and toughness. To alleviate this knowledge gap, this project is applying a systematic approach to measuring and analyzing sufficient and representative data of crack tip plasticity within single-phase RCCAs based on high throughput syntheses, rapid nano/micromechanical characterization techniques, physical modeling, and machine learning (ML) methods. Physical modeling results based on first-principles calculations, ML methods, and fracture mechanics are guiding the synthesis of RCCAs via high-throughput laser-directed energy deposition additive manufacturing (DED-AM). This approach uses a unique capability to enable rapid screening of compositions of up to six different elements. Nanomechanical characterization involving high-throughput nanoindentation and in situ direct pull tensile and notched cantilever fracture beam tests in a scanning electron microscope will be used for rapid assessment of tensile ductility, toughness, and deformation mechanisms at crack tips of RCCA samples. Finally, the physical modeling, deep learning of microstructural characterization images, and ductility measurements are being integrated to develop a ductility criterion beyond the classical Rice criterion for concentrated alloys that incorporates synergistic effects of multiple chemical elements and deformation mechanisms. Broader impact activities include: 1) enabling integrated-computational-materials-engineering (ICME) approaches and artificial intelligence (AI) concepts to be widely shared with senior undergraduate and graduate students, 2) championing outreach activities for students in K-12 students as well as 3) supporting opportunities for students of underrepresented groups to engage in state-of-the-art engineered materials research.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

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