activity
20202024
most citedFatigue Behavior of High-Entropy Alloys

2 citations · 3 across the 7 of their papers we have counts for

collaborators

7 papers

cond-mat.mtrl-sci2024★ 2 cited

Fatigue Behavior of High-Entropy Alloys

Shiyi Chen, Xuesong Fan, Hugh Shortt +3

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%). Dif…

cond-mat.mtrl-sci2023★ 1 cited

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

Jie Qi, Xuesong Fan, Diego Ibarra Hoyos +3

Refractory high-entropy alloys, RHEAs, are promising high-temperature structural materials. Their large compositional space poses great design challenges for phase control and high…

cond-mat.mtrl-sci2023

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

Baldur Steingrimsson, Ankur Agrawal, Xuesong Fan +3

The authors present a generic framework for the parameter optimization of additive manufacturing (AM) processes, one tailored to a high-throughput experimental methodology (HTEM).…

cond-mat.mtrl-sci2023

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

Jie Qi, Xuesong Fan, Diego Ibarra Hoyos +2

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…

physics.app-ph2022

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

Baldur Steingrimsson, Xuesong Fan, Benjamin Adam +1

In the pursuit of developing high-temperature alloys with improved properties for meeting the performance requirements of next-generation energy and aerospace demands, integrated c…

cond-mat.mtrl-sci2022

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

Baldur Steingrimsson, Xuesong Fan, Rui Feng +1

Machine learning is becoming a powerful tool to predict temperature-dependent yield strengths (YS) of structural materials, particularly for multi-principal-element systems. Howeve…

cond-mat.mtrl-sci2020

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

Baldur Steingrimsson, Xuesong Fan, Anand Kulkarni +2

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 p…