The evolving landscape for alloy design
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Introduction The discovery and development of new metallic alloys with unique properties and functionalities have revolutionized entire industries (e.g., aviation, space, communications, automotive, biomedical, and architecture), continuing the centuries-long trend for materials to fundamentally transform society (i.e., the bronze, iron, and silicon ages). Their design has traditionally been experimentally intensive, with linear exploration of composition space or via “design of experiments” approaches. Often with five to 10 major elements and as many “minor” elements, the compositional landscape is prohibitively large. Thus the design and development process has been slow and expensive, and more recently, unable to keep pace with the design tools of other engineering disciplines.1–5 To a greater degree than many other classes of materials, metallic alloys are challenged by a strong interdependence of processing, structure, and properties (PSP) across length scales, from atomic (Å) to the nanoscale (nm), spanning the microscale (μm) and extending up to the macroscale (>mm) (Figure 1). In order to reliably produce materials that perform in a predictable manner in service, it is essential for the PSP linkages to be predictable. These linkages have traditionally been established via experiments and characterization. For example, as demonstrated by the right pillar of Figure 1, the influence of forging parameters on grain structure and texture
and the resultant yield strength and fatigue life of an alloy would be understood by varying temperatures and strain rates during forging, measuring grain size and texture by electron backscatter imaging in a scanning electron microscope (SEM), and machining test specimens to establish yield strength and S-N curves as a function of temperature. Characterization results, processing information, and the results of testing would be stored in different data formats, most likely without metadata and in different physical locations. In recent years, there has been a dramatic expansion in our ability to predict PSP relationships, through improved theory, expanding suites of models, and a dramatic expansion in our ability to generate, archive, federate, and analyze materials data.1–3,6,7 These emerging capabilities provide two additional foundational “pillars” (Figure 1) that promise to dramatically change the landscape for alloy design. New developments along all three pillars (theory/modeling, data, and experiments/characterization) have been in large part enabled by an unprecedented expansion in computational power over the past decade. Thus, the aspirational vision to design new alloys “on demand” with the ability to predict their properties within statistically significant confidence limits is now within reach. In this article, we highlight examples of the progress along each of the pillars and also call attention to some of the many remaining gaps in the infrastructure.
Tresa M. Pollock, Department of Materials, University of California, Santa Barbara, USA; [email protected] Anton
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