The history of machine learning (ML) can be traced back to the 1950 s, and its application in
alloy design has recently begun to flourish and expand rapidly. The driving force behind this …

In the present work, the capability of artificial neural network (ANN) has been evaluated to
describe and to predict the high temperature flow behavior of a cast AZ81 magnesium alloy …

The objective of present study was to develop a medium-Mn steel with superior tensile
properties using machine learning. For this purpose, 1075 datasets on tensile properties of …

Artificial intelligence is widely employed in metallurgy for its ability to solve complex
phenomena, which are associated with the learning process of previously obtained …

Performance of TRIP (transformation induced plasticity) aided steel depends on the amount
and stability of retained austenite in the final microstructure. In the current work, a small …

A neural network with feed-forward topology and back propagation algorithm was used to
predict the effects of chemical composition and tensile test parameters on hardness of heat …

Data-driven method utilizes machine learning (ML) to mine hidden rules in data, conforming
to the" fourth paradigm". A great deal of basic data is needed for this method. By comparing …

Oxide scales have significant influences on the surface qualities of hot-rolled steels. In the
present work, an oxidation kinetic model for anisothermal conditions was employed with the …

In high-strength rebar, the various microstructures obtained by the Tempcore process and
the addition of V have a complex effect on the strength improvement of rebar. This study …

Transformation-induced plasticity (TRIP) Ti alloys are promising structural materials that offer
high strength and ductility. However, these alloys often include heavy, expensive, and high …