Abstract
Many of the useful properties of modern engineering materials are determined by the material’s microstructure. Controlling the microstructure requires an understanding of the complex dynamics underlying its evolution during processing. Investigating the thermal and mass transport phenomena responsible for a structure requires establishing a common language to quantitatively represent the microstructures being examined. Although such a common language exists for some of the simple structures, which has allowed these materials to be engineered, there has yet to be a method to represent complex systems, such as the ternary microstructures, which are important for many technologies. Here we show how stereological and data science methods can be combined to quantitatively represent ternary eutectic microstructures relative to a set of exemplars that span the stereological attribute space. Our method uniquely describes ternary eutectic microstructures, allowing images from different studies, with different compositions and processing histories, to be quantitatively compared. By overcoming this long-standing challenge, it becomes possible to begin to make progress toward a quantitatively predictive theory of ternary eutectic growth. We anticipate that the method of quantifying instances of an object relative to a set of exemplars spanning attribute-space will be broadly applied to classify materials structures, and may also find uses in other fields.
Highlights
Materials design involves observing and cataloging materials structures, understanding the underlying relationship between the multilevel structures and resulting material properties, and developing processing routes to prepare materials with the properties that yield optimal engineering performance[1]
Initial classification efforts described ternary eutectic microstructures as a combinations of the lamellar and rod morphologies observed in the regular binary eutectic microstructures[21,22,23], but this approach was unable to represent the multitude of complex morphologies observed in experiments[24,25,26,27,28]
The most widely used classification scheme for ternary eutectic morphologies is given by Ruggiero and Rutter[29] and its analytical solution is an extension of Jackson and Hunt’s analytical solution of binary eutectics[30]; three distinct growth modes are identified: semi-regular brick (SRB), lamellar (LAM), and rod-hexagon (RHN)[31,32]
Summary
Materials design involves observing and cataloging materials structures, understanding the underlying relationship between the multilevel structures and resulting material properties, and developing processing routes to prepare materials with the properties that yield optimal engineering performance[1] It relies upon the existence of a universally agreed upon language to quantitatively represent and subsequently catalog the observed structures[2]. A small set of geometric features, such as fixed eutectic spacings and the fixed spacing ratio of phases are used to describe the relative scale of these microstructures These approaches, yielding important insights, have yet to produce a universal representation of ternary eutectic microstructures that can be used to develop a predictive model of solidification. The greatest challenge for developing a quantitatively correct theory of ternary eutectic solidification is the creation of a universal language to allow a representation of the observed structures
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
