While experiments show that refining the prior austenite grain size can either accelerate or decelerate bainite formation in steels, kinetic models based on the successive nucleation of bainitic ferrite subunits can only predict an acceleration. In this work we develop a physically-based model for bainite kinetics assuming a displacive growth mechanism which is able to reproduce both faster and slower bainite formation kinetics induced by austenite grain refinement. A theoretical analysis of the model and comparison against published experimental data show that slower kinetics for smaller grains is favored as the difference between the activation energy for grain boundary and autocatalytic nucleation of bainite increases, and as the austenite grain refinement results in finer bainite sub-units. We also theoretically analyze the density of initially present potential nucleation sites for bainite and show that the values of density used in other published bainite nucleation models are mostly underestimated. After using physically consistent values for the density of potential nucleation sites, we were able to calculate the apparent lengthening rate of bainite sheaves which were in line with experimentally measured lengthening rates.

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In situ composite manufacture is an approach to improve interfacial adhesion between matrix and reinforcements, in which reinforcements are synthesized along composite processing itself. In situ powder metallurgy route, in particular, offers alternatives to some shortcomings found in other techniques. This work aims not only to review the state of the art on metal matrix composites (MMCs)—including cermets—obtained in situ by powder metallurgy, but also to dissect key aspects related to the development of such materials in order to establish theoretical criteria for decision making before and along experiments. Aspects regarding the design, raw material selection, and processing of such composites were observed and divided between concept, intrinsic, and extrinsic parameters. That way, by means of material databases and computational thermodynamics applied to examples of the reviewed literature, we aim at providing tools in both conducting leaner experiments and richer discussion in this field.@en