A systematic framework for choosing the most determinant combination of predictor features and solving the multiclass phase classification problem associated with high-entropy alloy (HEA) was recently proposed [1] . The data associated with that research paper, titled “ Machine learning-based prediction of phases in high-entropy alloys ”, is presented in this data article. This dataset is a systematic documentation and comprehensive survey of experimentally reported HEA microstructures. It contains microstructural phase experimental observations and metallurgy-specific features as introduced and reported in peer-reviewed research articles. The dataset is provided with this article as a supplementary file. Since the dataset was collected from experimental peer-reviewed articles, these data can provide insights into the microstructural characteristics of HEAs, can be used to improve the optimization HEA phases, and have an important role in machine learning, material informatics, as well as in other fields.

No abstract

The characterisation and monitoring of Ti6Al4V (ELI) feedstock powder is an essential requirement for the full qualification of medical implants and aerospace components produced in selective laser melting systems. Virgin and reused samples of this powder were characterised by determining their physical and chemical properties through techniques complying with international standards. This paper presents the results obtained for Ti6Al4V (ELI) powder of two different particle size distributions received from the same supplier. The characteristics of these powders after several reuse cycles in two different selective laser melting systems are also presented and discussed. OPSOMMINGDie karakterisering en kontrolering van Ti6Al4V voermateriaalpoeier is ʼn kernvereiste vir die kwalifisering van mediese implantate en ruimtevaartkomponente wat deur middel van selektiewe laser smelt stelsels produseer word. Nuwe en herbruikte monsters van hierdie poeier is gekarakteriseer deur hul fisiese en chemiese eienskappe te bepaal deur tegnieke wat aan internasionale standaarde voldoen. Die resultate vir Ti6Al4V poeiers met verskillende partikelgrootteverdelings van dieselfde verskaffer word hier voorgehou. Die karakteristieke van hierdie poeiers na etlike hergebruik siklusse in twee verskillende selektiewe laser smelt stelsels word ook voorgehou en bespreek.

No abstract

No abstract

No abstract

In order to reliably design and operate different powder processes, an understanding of the dynamic flow, shear and bulk properties of powders is required. Generally, powders are evaluated by several techniques that determine their flow, shear and bulk properties. The techniques can include compression tests, shear tests, angle of repose, flow of powder in a funnel, tapped density and many others. In order to minimize the number of instruments required to characterise the powder and eliminate operator error, automated powder rheometers that can do most of the required tests have been developed. The FT4 powder rheometer is one of these and has found widespread use in the pharmaceutical industry. In this study, the FT4 powder rheometer was used to characterise two metallic titanium powders with different particle sizes, namely CSIR Ti-45μm (Fine Powder) and CSIR Ti +45-180μm (Coarse Powder). Their particle size, particle size distribution, bulk densities, compressibility, cohesion, flowability index, effective angle of internal friction and wall friction angle were determined. Preliminary results of the study indicated that fine powder had a lower bulk density, was more compressible and more cohesive than the coarse powder. The fine powder had a lower flowability index compared to the coarse powder for both the Jenike and Peschl classification. The varying degrees of cohesion of these powders were confirmed by the cohesion values that were higher for the fine powder. The fine powder had a lower angle of internal friction but higher wall friction angle compared to the coarse powder.

No abstract