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Microstructure Sensitive Design for Performance Optimization
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TLDR
This review presents the MSD framework in the context of both the engineering advances that have led to its creation, and those that complement or provide alternative methods for design of materials (meaning ‘optimization of material structure’ in this context).Abstract:
The accelerating rate at which new materials are appearing, and transforming the engineering world, only serves to emphasize the vast potential for novel material structure, and related performance. Microstructure-sensitive design (MSD) aims at providing inverse design methodologies that facilitate design of material internal structure for performance optimization. Spectral methods are applied across the structure, property and processing design spaces in order to compress the computational requirements for linkages between the spaces and enable inverse design. Research has focused mainly on anisotropic, polycrystalline materials, where control of local crystal orientation can result in a broad range of property combinations. This review presents the MSD framework in the context of both the engineering advances that have led to its creation, and those that complement or provide alternative methods for design of materials (meaning ‘optimization of material structure’ in this context). A variety of definitions for the structure of materials are presented, with an emphasis on correlation functions; and spectral methods are introduced for compact descriptions and efficient computations. The microstructure hull is defined as the design space for structure in the spectral framework. Reconstruction methods provide invertible links between statistical descriptions of structure, and deterministic instantiations. Subsequently, structure–property relations are reviewed, and again subjected to representation via spectral methods. The concept of a property closure is introduced as the design space for performance optimization, and methods for moving between the closures and hulls are presented as the basis for the subsequent discussion on microstructure design. Finally, the spectral framework is applied to deformation processes, and methodologies that facilitate process design are reviewed.read more
Citations
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Versatile algorithms for the computation of 2-point spatial correlations in quantifying material structure
TL;DR: This paper presents a generalized framework along with the associated computational strategies for a rigorous quantification of the material structure in a range of different applications using the framework of 2-point spatial correlations for applications requiring different assumptions about the periodicity and/or involving irregular domain shapes and potentially extremely large datasets.
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Improved representations of misorientation information for grain boundary science and engineering
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Extraction of reduced-order process-structure linkages from phase-field simulations
Yuksel C. Yabansu,Philipp Steinmetz,Johannes Hötzer,Johannes Hötzer,Surya R. Kalidindi,Britta Nestler,Britta Nestler +6 more
TL;DR: In this paper, a low-dimensional representation of material structure obtained through principal component analysis (PCA) of 2-point spatial correlations is presented for phase-field simulations of ternary eutectic alloys.
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Analytics for microstructure datasets produced by phase-field simulations
Philipp Steinmetz,Yuksel C. Yabansu,Johannes Hötzer,Johannes Hötzer,Marcus Jainta,Britta Nestler,Britta Nestler,Surya R. Kalidindi +7 more
TL;DR: It is demonstrated that the recently developed framework for the quantification of the material structure, based on the concepts of 2-point spatial correlations and principal component analyses (PCA), can address this critical need.
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