It has been broadly reported that determination of the martensite start temperature in steels, M s , requires a complete description of their chemical composition. Recently, several neural networks models considering both chemical composition and austenite grain size (AGS) have been developed. Such models predict a moderate dependence of M s with AGS. The present work examines the validity of existing neural network models, but focusing on fine AGS (below 5 m).

High-entropy alloys (HEAs) have attracted a great deal of interest over the last 14 years. One reason for this level of interest is related to these alloys breaking the alloying principles that have been applied for many centuries. Thus, HEAs usually possess a single phase (contrary to expectations according to the composition of the alloy) and exhibit a high level of performance in different properties related to many developing areas in industry. Despite this significant interest, most HEAs have been developed via ingot metallurgy. More recently, powder metallurgy (PM) has appeared as an interesting alternative for further developing this family of alloys to possibly widen the field of nanostructures in HEAs and improve some capabilities of these alloys. In this paper, PM methods applied to HEAs are reviewed, and some possible ways to develop the use of powders as raw materials are introduced.

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