Predicting temperature-dependent ultimate strengths of body-centered-cubic (BCC) high-entropy alloys

Abstract

This paper presents a bilinear log model, for predicting temperature-dependent ultimate strength of high-entropy alloys (HEAs) based on 21 HEA compositions. We consider the break temperature, Tbreak, introduced in the model, an important parameter for design of materials with attractive high-temperature properties, one warranting inclusion in alloy specifications. For reliable operation, the operating temperature of alloys may need to stay below Tbreak. We introduce a technique of global optimization, one enabling concurrent optimization of model parameters over low-temperature and high-temperature regimes. Furthermore, we suggest a general framework for joint optimization of alloy properties, capable of accounting for physics-based dependencies, and show how a special case can be formulated to address the identification of HEAs offering attractive ultimate strength. We advocate for the selection of an optimization technique suitable for the problem at hand and the data available, and for properly accounting for the underlying sources of variations.

A low-density high-entropy dual-phase alloy with hierarchical structure and exceptional specific yield strength

“…It is worth mentioning that there is a temperature-dependent strength of the materials with high-temperature applications. Steingrimsson et al [59] proposed a break temperature (T break ) for predicting the high-temperature strengths in BCC HEAs, using the relationship between the strength and temperature in BCC HEAs. Steingrimsson et al's model [59] is for the ultimate strength.…”

Properties and processing technologies of high-entropy alloys

“…Data analytics and machine learning can help effectively achieve rapid screening in vast compositional space. Steingrimsson et al have successfully predicted temperature-dependent ultimate strengths of bcc HEAs via machine learning [46]. They proposed a bilinear log model for predicting the ultimate strengths of HEAs under different temperatures, evaluating effectiveness by 21 compositions.…”

A Focused Review on Engineering Application of Multi-Principal Element Alloy

“…For MPEAs containing multiple principal element, the addition of more ferromagnetic elements makes it easier to have excellent magnetic properties. There are many studies on the magnetic properties of MPEAs obtained by adding two elements with an equal molar ratio to the FeCoNi matrix, including FeCoNi(A1Si)x (Zhang et al, 2013), FeCoNi(MnSi)x (Li et al, 2019), FeCoNi(NiAl)x (Zhang X. K. et al, 2021), FeCoNi(CuAl)x (Zhang Q. et al, 2017), and FeCoNi (MnAl)x (Li et al, 2017a).…”

“…The optimum CoCrNi MPEA can be obtained (Figure 12A). More interestingly, some researchers tried to construct an artificial neural networks-like structure to describe the relationship between "composition-heat treatmentsmicrostructures-properties", for revealing their potential physics mechanism (Steingrimsson et al, 2021). Another important application is to identity and/or optimize the microstructure of MPEAs.…”