Application of Microstructure Sensitive Design to FCC Polycrystals (original) (raw)
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Computational Materials Science, 2008
In this paper, we present the first successful design case studies in the application of microstructure sensitive design (MSD) methodology to optimize performance of structural components made from polycrystalline metals with hexagonal close-packed (hcp) crystal lattices. It is demonstrated that the underlying spectral framework of the MSD methodology facilitates an efficient consideration of the complete set of crystallographic textures in the design optimization. In order to accomplish this task a number of important enhancements had to be introduced to the MSD framework. The most significant enhancement is in the mathematical description of the design space, i.e. the texture hull. The advantages of the new approach described in this paper are illustrated with two specific design case studies involving different assumptions of symmetry at the sample scale. In both case studies presented, it is seen that the overall performance is strongly influenced by the crystallographic texture in the sample. Furthermore, the relevant property closures and performance maps accounting for the complete set of textures are also depicted. Published by Elsevier B.V.
Computational Design of Microstructure: An overview
During the last couple of decades, treatment of microstructure in materials science has been shifted from the diagnostic to design paradigm. Design of microstructure is inherently complex problems due to non linear spatial and temporal interaction of composition and parameters leading to the target properties. In most of the cases, different properties are reciprocally correlated i.e., improvement of one lead to the degradation of other. Also, the design of microstructure is a multiscale problem, as the knowledge of phenomena at range of scales from electronic to mesoscale is required for precise composition-microstructure-property determination. In the view of above, present chapter provides the introduction to computationally driven microstructure engineering in the framework of constitutive length scale in microstructure design. The important issues pertaining to design such as phase stability and interfaces has been explained. Additionally, the bird-eye view of various computational techniques in order of length scale has been introduced, with an aim to present the picture of combination of various techniques for solving microstructural design problems under various scenarios.
International Journal of Plasticity, 2010
Microstructure sensitive design (MSD) has thus far focused mainly on the identification of the set of microstructures that are theoretically predicted to exhibit a designer-specified combination of elastic-plastic properties. In this paper, we present the extension of the MSD methodology to process design solutions. The goal of process design is to identify a processing route to transform a given initial microstructure into a different microstructure that exhibits superior property combinations by using an arbitrary sequence of available deformation processing options (hereafter referred to as hybrid processing routes). In this paper, we have focused on orientation distribution function (i.e. the 1-point statistics of crystallographic texture in the sample) as the descriptor of microstructure, and considered only the low temperature deformation processes. We have also restricted our attention to Taylor-type crystal plasticity models. With these idealizations, it is shown that it is possible to develop efficient algorithms in the MSD framework to build texture evolution networks that cover most of the texture hull. The advantages of this approach are expounded upon in this paper with selected case studies.
Computational Design of Microstructure
Computational Approaches to Materials Design
During the last couple of decades, treatment of microstructure in materials science has been shifted from the diagnostic to design paradigm. Design of microstructure is inherently complex problems due to non linear spatial and temporal interaction of composition and parameters leading to the target properties. In most of the cases, different properties are reciprocally correlated i.e., improvement of one lead to the degradation of other. Also, the design of microstructure is a multiscale problem, as the knowledge of phenomena at range of scales from electronic to mesoscale is required for precise compositionmicrostructure-property determination. In the view of above, present chapter provides the introduction to computationally driven microstructure engineering in the framework of constitutive length scale in microstructure design. The important issues pertaining to design such as phase stability and interfaces has been explained. Additionally, the bird-eye view of various computational techniques in order of length scale has been introduced, with an aim to present the picture of combination of various techniques for solving microstructural design problems under various scenarios.
A Hybrid Bishop-Hill Model for Microstructure Sensitive Design
2012
A Hybrid Bishop-Hill Model for Microstructure Sensitive Design Ribeka Takahashi Department of Mechanical Engineering, BYU Doctor of Philosophy A method is presented for adapting the classical Bishop-Hill model to the requirements of elastic/yield-limited design in metals of arbitrary crystallographic texture. The proposed Hybrid Bishop-Hill (HBH) model, which will be applied to ductile FCC metals, retains the ‘stress corners’ of the polyhedral Bishop-Hill yield surface. However, it replaces the ‘maximum work criterion’ with a criterion that minimizes the Euclidean distance between the applicable local corner stress state and the macroscopic stress state. This compromise leads to a model that is much more accessible to yield-limited design problems. Demonstration of performance for the HBH model is presented for an extensive database for oxygen free electronic (OFE) copper. The study also implements the HBH model to the polycrystalline yield surface via standard finite element analys...
ATLAS of yield surfaces for strongly textured FCC polycrystals
PROCEEDINGS OF THE 22ND INTERNATIONAL ESAFORM CONFERENCE ON MATERIAL FORMING: ESAFORM 2019
Discrete yield surfaces for several generic texture components, including randomness, found in aluminium alloys have been generated and used to calibrate the yield function Yld2004-18p. It is generally observed that the roundness of the corners of the yield surface increases, and the stress and strain ratios flatten towards isotropic values, as the ratio of random component increases. A short investigation on the effect of number of points and homogenization approach on calibration of the yield function seems to indicate that the number of points used in the calibration has a stronger effect than the homogenization approach. Furthermore, it is also shown that setting the exponent as a free parameter in calibrating the yield function could lead to better fits.
A Descriptor-Based Design Methodology for Developing Heterogeneous Microstructural Materials System
Journal of Mechanical Design, 2014
In designing a microstructural materials system, there are several key questions associated with design representation, design evaluation, and design synthesis: how to quantitatively represent the design space of a heterogeneous microstructure system using a small set of design variables, how to efficiently reconstruct statistically equivalent microstructures for design evaluation, and how to quickly search for the optimal microstructure design to achieve the desired material properties. This paper proposes a new descriptor-based methodology for designing microstructural materials systems. It is proposed to use a small set of microstructure descriptors to represent material morphology features quantitatively. The descriptor set should be able to cover microstructure features at different levels, including composition, dispersion status, and phase geometry. A descriptor-based multiphase microstructure reconstruction algorithm is developed accordingly that allows efficient stochastic reconstructions of microstructures in both 2D and 3D spaces for finite element analysis (FEA) of material behavior. Finally, the descriptor-based representation allows the use of parametric optimization approach to search the optimal microstructure design that meets the target material properties. To improve the search efficiency, this paper integrates state-of-the-art computational design methods such as design of experiment (DOE), metamodeling, statistical sensitivity analysis, and multi-objective optimization, into one design optimization framework to automate the microstructure design process. The proposed methodology is demonstrated using the design of a polymer nanocomposites system. The choice of descriptors for polymer nanocomposites is verified by establishing a mapping between the finite set of descriptors and the infinite dimensional correlation function.
On design of multi-functional microstructural materials
Journal of Materials Science, 2012
The design of periodic microstructural composite materials to achieve specific properties has been a major area of interest in material research. Tailoring different physical properties by modifying the microstructural architecture in unit cells is one of the main concerns in exploring and developing novel multi-functional cellular composites and has led to the development of a large variety of mathematical models, theories and methodologies for improving the performance of such materials. This paper provides a critical review on the state-of-the-art advances in the design of periodic microstructures of multi-functional materials for a range of physical properties, such as elastic stiffness, Poisson's ratio, thermal expansion coefficient, conductivity, fluidic permeability, particle diffusivity, electrical permittivity and magnetic permeability, etc.