Dataset for Fracture and Impact Toughness of High-Entropy Alloys

Abstract

Fracture dictates the service limits of metallic structures. Damage tolerance of materials may be characterized by fracture toughness rigorously developed from fracture mechanics, or less rigorous yet more easily obtained impact toughness (or impact energy as a variant). Given the promise of high-entropy alloys (HEAs) in structural and damage-tolerance applications, we compiled a dataset of fracture toughness and impact toughness/energy from the literature till the end of the 2022 calendar year. The dataset is subdivided into three categories, i.e., fracture toughness, impact toughness, and impact energy, which contain 153, 14, and 78 distinct data records, respectively. On top of the alloy chemistry and measured fracture quantities, each data record also documents the factors influential to fracture. Examples are material-processing history, phase structures, grain sizes, uniaxial tensile properties, such as yield strength and elongation, and testing conditions. Data records with comparable conditions are graphically visualized by plots. The dataset is hosted in Materials Cloud, an open data repository.

Fatigue database of complex metallic alloys

“…Comprehensive inspection can thus be replaced by sampling inspection, which significantly saves labor work considering the large volume of the scientific literature corpus. To guide the construction of a high-quality database, the sampling strategy is determined based on the accuracy of automated data extraction ( α a ) or manual examination ( α m ) reported in our work and other high-quality literature-informed database 36 , 38 , 39 , 43 . For a target accuracy ( α t ), the size of sampling set relative to the complete datasets ( s ) and the rounds of examination ( n r ) are determined by the condition of .…”

“…Data science is of crucial importance in the research of MGs and MPEAs for the vast design space compared to conventional alloys. Experimentally measured 36 , 38 , 39 and theoretically predicted 40 , 41 data reported in the literature and research reports are valuable sources to understand and improve the performance of alloys. Text mining, image data processing, and artificial intelligence 37 , 42 techniques are expected to offer insights into the CPMP relationship as demonstrated in our recent work on AM alloys 43 .…”

Deformation Behavior of 3D‐Printed High‐Entropy Alloys: A Critical Review

“…[6] According to this notion, as multiple metallic elements are mixed, the tendency of forming intermetallic compounds is suppressed by the increased configurational entropy, which ultimately lowers the Gibbs free energy to form thermodynamically stable solid solutions called "high-entropy alloys" (HEAs). [1] Since their inception, the HEAs have triumphed over almost all domains of mechanical properties, including strength-ductility synergy, [7,8] fatigue [9] and fracture resistance, [10,11] dynamic response to ballistic impact, [12] wear and tribological performance, [13][14][15] mechanical integrity at high [16] and cryogenic [17] temperatures, weldability, [18,19] radiation resistance, [20,21] corrosion and oxidation resistance, [22,23] etc. Consequently, the upcoming breakthroughs in this era's vitally crucial domains of nuclear energy, [24] automotive and aerospace are foreseeable through HEA design.…”

Diseño de aleación de alta entropía de alto desempeño mecánico en condiciones de bajas y altas temperaturas para aplicaciones energéticas y aeroespaciales.

“…Sin embargo, este trabajo busca reducir sesgos de predicción. Basado en lo expuesto anteriormente, se descompuso la variable objetivo en ocho clases con su respectiva distribución de datos, las cuales son: FCC (496), BCC (617), BCC+FCC (197), FCC+Im (277), BCC+Im (205), FCC+BCC+Im (76), Im (368) y AM (198).…”