arXiv subjects

Xuesong Fan

Publications and source records attributed to Xuesong Fan.

7 recordsLinked to original sources

arXiv2024

Fatigue Behavior of High-Entropy Alloys

High-entropy alloys (HEAs) refer to alloys composed of five or more elements in equal or near-equal amounts or in an atomic concentration range of 5 to 35 atomic percent (at%). Different elemental ratios will affect the microstructures of HEAs and provide them with unique properties. Based on past research, HEAs have exhibited superior performance, relative to most conventional alloys, with respect to many properties, such as strength, toughness, corrosion resistance, magnetic behavior, etc. Among them, fatigue behavior has been a topic of focus, due to its importance in industrial applications. In this article, we summarized the research progress in the HEA-fatigue behavior in the past ten years, including experimental results and theoretical studies in subdivisions, such as high-cycle fatigue, low-cycle fatigue, fatigue-crack growth, fatigue mechanisms, etc. The influence of the processing and test methods on HEAs is described. The accuracy of several commonly used prediction models is also outlined. Finally, unresolved issues and suggestions on the direction of future research efforts are presented.

cond-mat.mtrl-sci↗

arXiv2023

Integrated Design of Aluminum-Containing High-entropy Refractory B2 Alloys with Synergy of High Strength and Ductility

Refractory high-entropy alloys, RHEAs, are promising high-temperature structural materials. Their large compositional space poses great design challenges for phase control and high strength-ductility synergy. The present research pioneers using integrated high-throughput machine learning with Monte Carlo simulations to effectively navigate phase-selection and mechanical-properties predictions, developing aluminum-containing RHEAs in single-phase ordered B2 alloys demonstrating both high strength and ductility. These aluminum-containing RHEAs achieve remarkable mechanical properties, including compressive yield strengths up to 1.6 GPa, fracture strains exceeding 50 percent, and significant high-temperature strength retention. They also demonstrate a tensile yield strength of 1.1 GPa with a tension ductility of 6.3 percent. Besides, we identify a valence-electron-count domain for alloy brittleness with the explanation from density-functional theory and provide crucial insights into elements' influence on atomic ordering and mechanical performance. The work sets forth a strategic blueprint for high-throughput alloy design and reveals fundamental principles that govern the mechanical properties of advanced structural alloys.

cond-mat.mtrl-sci↗

arXiv2023

Construction of Multi-Dimensional Functions for Optimization of Additive-Manufacturing Process Parameters

The authors present a generic framework for the parameter optimization of additive manufacturing (AM) processes, one tailored to a high-throughput experimental methodology (HTEM). Given the large number of parameters, which impact the quality of AM-metallic components, the authors advocate for partitioning the AM parameter set into stages (tiers), based on their relative importance, modeling one tier at a time until successful, and then systematically expanding the framework. The authors demonstrate how the construction of multi-dimensional functions, based on neural networks (NN), can be applied to successfully model relative densities and Rockwell hardness obtained from HTEM testing of the Inconel 718 superalloy fabricated, using a powder-bed approach. The authors analyze the input data set, assess its suitability for predictions, and show how to optimize the framework for the multi-dimensional functional construction, such as to obtain the highest degree of fit with the input data set. The authors also compare and contrast the NN-based multi-dimensional functional construction to multi-variate linear regression, to polynomial regression, and to Gaussian process regression (GPR), highlight similarity between the NN-based multi-dimensional functional construction and the GPR, and offer insights into the suitability of each of these methods for the data set and the application at hand. In terms of the coefficient of determination, $R^2$, a relatively simple, single-layer NN with 5 or 10 nodes outperforms multi-variate linear regression, 2nd-order polynomial regression, and GPR for the primary Inconel 718 HTEM data set studied. The novelty of the research work entails the versatile and scalable NN framework presented, suitable for use in conjunction with HTEM, for the AM parameter optimization of superalloys, and beyond.

cond-mat.mtrl-sci↗

arXiv2023

High Strength Refractory AlHfNbTiV B2 High Entropy Alloys with High Fracture Strains

We demonstrate the development of a series of refractory high-entropy alloys containing aluminum AlRHEAs in the ordered BCC-B2 phase by varying the aluminum content within 10 to 25 atomic percent, with the goal of high strength and good ductility synergy. The AlRHEAs obtained are found to show promising potential for high-temperature applications. The incorporation of Al lowers the density and promotes the long-range atomic ordering, which in turn stabilizes the B2 formation, and strengthens the material but usually deteriorates ductility. Several B2 AlRHEAs that contain a combination of Ti, Hf, Nb, and V with moderate to high Poisson ratios are investigated for high strength and ductility. Furthermore, through statistical analysis, we identify a valley around the valence electron concentration VEC of 6 where low ductility is prominently observed. Machine-learning models are employed to screen the vast compositional space of AlRHEA alloys to predict B2 formation and toughness indicated by the yield strength and fracture strain. High prediction accuracies are achieved. As the Al content decreases, the B2 atomic ordering decreases, compression yield strengths decrease from 1500 MPa to 1200 MPa, and compression fracture strains increase from 0.06 to over 0.5. Notably, Al10Hf20Nb22Ti33V15 retains a compression yield strength exceeding 800 MPa up to 700 C, tensile yield strength of 1100 MPa, and fracture strain of 0.083. Our findings on enhancing ductility in pure B2 alloys pave the way for further research on Al-RHEA superalloys, striving to achieve high strength and ductility, reduced density, and improved oxidation resistance.

cond-mat.mtrl-sci↗

arXiv2022

Physics-Based Machine Learning Approach for Modeling the Temperature-Dependent Yield Strength of Superalloys

In the pursuit of developing high-temperature alloys with improved properties for meeting the performance requirements of next-generation energy and aerospace demands, integrated computational materials engineering (ICME) has played a crucial role. In this paper a machine learning (ML) approach is presented, capable of predicting the temperature-dependent yield strengths of superalloys, utilizing a bilinear log model. Importantly, the model introduces the parameter break temperature, $T_{break}$, which serves as an upper boundary for operating conditions, ensuring acceptable mechanical performance. In contrast to conventional black-box approaches, our model is based on the underlying fundamental physics, directly built into the model. We present a technique of global optimization, one allowing the concurrent optimization of model parameters over the low-temperature and high-temperature regimes. The results presented extend previous work on high-entropy alloys (HEAs) and offer further support for the bilinear log model and its applicability for modeling the temperature-dependent strength behavior of superalloys as well as HEAs.

physics.app-ph↗

arXiv2022

Physics-Based Machine-Learning Approach for Modeling the Temperature-Dependent Yield Strengths of Medium- or High-Entropy Alloys

Machine learning is becoming a powerful tool to predict temperature-dependent yield strengths (YS) of structural materials, particularly for multi-principal-element systems. However, successful machine-learning predictions depend on the use of reasonable machine-learning models. Here, we present a comprehensive and up-to-date overview of a bilinear log model for predicting temperature-dependent YS of medium-entropy or high-entropy alloys (MEAs or HEAs). In this model, a break temperature, Tbreak, is introduced, which can guide the design of MEAs or HEAs with attractive high-temperature properties. Unlike assuming black-box structures, our model is based on the underlying physics, incorporated in form of a priori information. A technique of global optimization is employed to enable the concurrent optimization of model parameters over low- and high-temperature regimes, showing that the break temperature is consistent across YS and ultimate strength for a variety of HEA compositions. A high-level comparison between YS of MEAs/HEAs and those of nickel-based superalloys reveal superior strength properties of selected refractory HEAs. For reliable operations, the temperature of a structural component, such as a turbine blade, made from refractory alloys may need to stay below Tbreak. Once above Tbreak, phase transformations may start taking place, and the alloy may begin losing structural integrity.

cond-mat.mtrl-sci↗

arXiv2020

Machine Learning and Data Analytics for Design and Manufacturing of High-Entropy Materials Exhibiting Mechanical or Fatigue Properties of Interest

This chapter presents an innovative framework for the application of machine learning and data analytics for the identification of alloys or composites exhibiting certain desired properties of interest. The main focus is on alloys and composites with large composition spaces for structural materials. Such alloys or composites are referred to as high-entropy materials (HEMs) and are here presented primarily in context of structural applications. For each output property of interest, the corresponding driving (input) factors are identified. These input factors may include the material composition, heat treatment, manufacturing process, microstructure, temperature, strain rate, environment, or testing mode. The framework assumes the selection of an optimization technique suitable for the application at hand and the data available. Physics-based models are presented, such as for predicting the ultimate tensile strength (UTS) or fatigue resistance. We devise models capable of accounting for physics-based dependencies. We factor such dependencies into the models as a priori information. In case that an artificial neural network (ANN) is deemed suitable for the applications at hand, it is suggested to employ custom kernel functions consistent with the underlying physics, for the purpose of attaining tighter coupling, better prediction, and for extracting the most out of the - usually limited - input data available.

cond-mat.mtrl-sci↗