Design for Performance Optimization

Adams, Brent L.; Kalidindi, Surya R.; Fullwood, David T.

doi:10.1016/b978-0-12-396989-7.00010-1

Cited by 51 publications

(124 citation statements)

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“…For discrete data, the computational basis of spectral analysis is discrete Fourier transforms (DFTs). DFTs transform time-or space-based data into frequency-based data (Adams et al, 2012). Recently, a new DFT database based spectral approach has been developed Alharbi and Kalidindi, 2015;Knezevic and Kalidindi, 2017) to solve the crystal plasticity constitutive equations, which reduces the computational cost of performing crystal plasticity calculations tremendously (up to two orders of magnitude) by allowing compact representation and fast retrieval of crystal plasticity solutions.…”

Section: Crystal Plasticity Computations Using Database Of Dftsmentioning

confidence: 99%

“…The cost of recovering the solutions from this new database is roughly the same as the earlier database, which is still about two orders of magnitude faster than the conventional computations. The number of simulations required for generating DFT database was reduced by taking advantage of the symmetry relations for cubic crystals (Adams et al, 2012) and the mirror symmetry evident within the periodic domain of θ (see Fig. 2).…”

Section: Crystal Plasticity Computations Using Database Of Dftsmentioning

confidence: 99%

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Computationally efficient predictions of crystal plasticity based forming limit diagrams using a spectral database

Gupta

Bettaieb

Abed‐Meraim

et al. 2018

International Journal of Plasticity

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is an open access repository that collects the work of Arts et Métiers ParisTech researchers and makes it freely available over the web where possible.This is an author-deposited version published in: https://sam.ensam.eu Handle ID: .http://hdl.handle.net/10985/13136 To cite this version :Akash GUPTA, Mohamed BEN BETTAIEB, Farid ABED-MERAIM, Surya KALIDINDIComputationally efficient predictions of crystal plasticity based forming limit diagrams using a spectral database -International Journal of Plasticity -Vol. 103, p.168-187 -2018 Any correspondence concerning this service should be sent to the repository A B S T R A C TThe present investigation focuses on the development of a fast and robust numerical tool for the prediction of the forming limit diagrams (FLDs) for thin polycrystalline metal sheets using a Taylor-type (full constraints) crystal plasticity model. The incipience of localized necking is numerically determined by the well-known Marciniak-Kuczynski model. The crystal plasticity constitutive equations, on which these computations are based, are known to be highly nonlinear, thus involving computationally very expensive solutions. This presents a major impediment to the wider adoption of crystal plasticity theories in the computation of FLDs. In this work, this limitation is addressed by using a recently developed spectral database approach based on discrete Fourier transforms (DFTs). Significant improvements were made to the prior approach and a new database was created to address this challenge successfully. These extensions are detailed in the present paper. It is shown that the use of the database allows a significant reduction in the computational cost involved in crystal plasticity based FLD predictions (a reduction of about 96% in terms of CPU time).

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Section: Crystal Plasticity Computations Using Database Of Dftsmentioning

confidence: 99%

Section: Crystal Plasticity Computations Using Database Of Dftsmentioning

confidence: 99%

Computationally efficient predictions of crystal plasticity based forming limit diagrams using a spectral database

Gupta

Bettaieb

Abed‐Meraim

et al. 2018

International Journal of Plasticity

Self Cite

View full text Add to dashboard Cite

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“…Consequently, it is preferable to adopt a microstructure quantification framework that allows one to increase systematically the numbers of potential features included in the analyses. In this regard, the framework of n-point spatial correlations [12,53,54] offers tremendous promise because of its scalability (ability to define an infinite number of microstructural features), organization (value of n can start with one and increase systematically), and available access to efficient computational toolsets [55,56]. Another option for this step includes lineal path functions [57] or chord-length distributions [58,59] that provide information about shape and size distribution of a specific feature of interest.…”

Section: Data Science Workflow For Extracting Process-structure Linkagesmentioning

confidence: 99%

Process-Structure Linkages Using a Data Science Approach: Application to Simulated Additive Manufacturing Data

Popova

Rodgers

Gong

et al. 2017

Integr Mater Manuf Innov

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104

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A novel data science workflow is developed and demonstrated to extract process-structure linkages (i.e., reduced-order model) for microstructure evolution problems when the final microstructure depends on (simulation or experimental) processing parameters. This workflow consists of four main steps: data pre-processing, microstructure quantification, dimensionality reduction, and extraction/validation of process-structure linkages. Methods that can be employed within each step vary based on the type and amount of available data. In this paper, this data-driven workflow is applied to a set of synthetic additive manufacturing microstructures obtained using the Potts-kinetic Monte Carlo (kMC) approach. Additive manufacturing techniques inherently produce complex microstructures that can vary significantly with processing conditions. Using the developed workflow, a lowdimensional data-driven model was established to correlate process parameters with the predicted final microstructure. Additionally, the modular workflows developed and presented in this work facilitate easy dissemination and curation by the broader community.

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“…However, the two-step process [14] described above has enabled the rapid identification of MTR clusters, each distributed about a common texture component with a defined misorientation range (<10°in this case) within each cluster. The cluster analysis was conducted with a feature vector of 551 dimensions in domain of the generalized spherical harmonics (GSH), a mathematical construct commonly used to analytically describe the distribution of crystallographic orientations [16][17][18][19][20][21]. The normal direction (ND) inverse pole figure (IPF) maps in Figure 6a, b show the variability of the length scale from primary alpha grains in (b) to MTRs in (a).…”

Section: Identification Of Features Of Interestmentioning

confidence: 99%

“…In materials with complex structures, one has to identify suitable hierarchical length scales for homogenization, which are called regions of homogeneity. ROH can be established objectively at different hierarchical length scales by carefully quantifying spatial correlations (e.g., using two-point spatial correlations [17,[25][26][27][28][29][30]) and finding suitable window sizes in the microstructure that capture the inherent heterogeneity (FoI and their spatial distributions) to a desired accuracy. It is also desired to keep these ROH small enough to enable costeffective modeling (the computational cost rises steeply with increases in the number of voxels needed to capture the ROH).…”

Section: Representative Descriptions Of Microstructure Regions Of Hommentioning

confidence: 99%

Workflow for integrating mesoscale heterogeneities in materials structure with process simulation of titanium alloys

Salem

Shaffer

Satko

et al. 2014

Integr Mater Manuf Innov

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In this paper, a generalized workflow is outlined for the necessary integration of multimodal measurements and multiphysics models at multiple hierarchical length scales demanded by an Integrated Computational Materials Engineering (ICME) approach to accelerated materials development. Recognizing that multiple choices or techniques are typically available in each of the main steps, several exemplary analyses are detailed utilizing mainly the alpha/beta titanium alloys as an illustrative case. It is anticipated that the use and further refinement of these workflows will promote transparency and engender intimate collaborations between materials experts and manufacturing/design specialists by providing an understanding of the various mesoscale heterogeneities that develop naturally in the workpiece as a direct consequence of the inherent heterogeneity imposed by the manufacturing history (i.e., different thermomechanical histories at different locations in the sample). More specifically, this article focuses on three main areas: (i) data science protocols for efficient analysis of large microstructure datasets (e.g., cluster analysis), (ii) protocols for extracting reduced descriptions of salient microstructure features for insertion into simulations (e.g., regions of homogeneity), and (iii) protocols for direct and efficient linking of materials models/databases into process/performance simulation codes (e.g., crystal plasticity finite element method).

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Design for Performance Optimization

Cited by 51 publications

References 0 publications

Computationally efficient predictions of crystal plasticity based forming limit diagrams using a spectral database

Computationally efficient predictions of crystal plasticity based forming limit diagrams using a spectral database

Process-Structure Linkages Using a Data Science Approach: Application to Simulated Additive Manufacturing Data

Workflow for integrating mesoscale heterogeneities in materials structure with process simulation of titanium alloys

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