Research Papers: Design Automation

Multi-Information Source Fusion and Optimization to Realize ICME: Application to Dual-Phase Materials

[+] Author and Article Information
Seyede Fatemeh Ghoreishi

Department of Mechanical Engineering,
Texas A&M University,
College Station, TX 77843
e-mail: f.ghoreishi88@tamu.edu

Abhilash Molkeri

Department of Materials Science
and Engineering,
Texas A&M University,
College Station, TX 77843
e-mail: abhilashmolkeri@tamu.edu

Ankit Srivastava

Department of Materials Science and
Texas A&M University,
College Station, TX 77843
e-mail: ankit.sri@tamu.edu

Raymundo Arroyave

Department of Materials Science and
Texas A&M University,
College Station, TX 77843
e-mail: rarroyave@tamu.edu

Douglas Allaire

Department of Mechanical Engineering,
Texas A&M University,
College Station, TX 77843
e-mail: dallaire@tamu.edu

1Corresponding author.

Contributed by the Design Automation Committee of ASME for publication in the JOURNAL OF MECHANICAL DESIGN. Manuscript received March 31, 2018; final manuscript received July 24, 2018; published online September 7, 2018. Assoc. Editor: Yan Wang.

J. Mech. Des 140(11), 111409 (Sep 07, 2018) (14 pages) Paper No: MD-18-1271; doi: 10.1115/1.4041034 History: Received March 31, 2018; Revised July 24, 2018

Integrated Computational Materials Engineering (ICME) calls for the integration of computational tools into the materials and parts development cycle, while the Materials Genome Initiative (MGI) calls for the acceleration of the materials development cycle through the combination of experiments, simulation, and data. As they stand, both ICME and MGI do not prescribe how to achieve the necessary tool integration or how to efficiently exploit the computational tools, in combination with experiments, to accelerate the development of new materials and materials systems. This paper addresses the first issue by putting forward a framework for the fusion of information that exploits correlations among sources/models and between the sources and “ground truth.” The second issue is addressed through a multi-information source optimization framework that identifies, given current knowledge, the next best information source to query and where in the input space to query it via a novel value-gradient policy. The querying decision takes into account the ability to learn correlations between information sources, the resource cost of querying an information source, and what a query is expected to provide in terms of improvement over the current state. The framework is demonstrated on the optimization of a dual-phase steel to maximize its strength-normalized strain hardening rate. The ground truth is represented by a microstructure-based finite element model while three low fidelity information sources—i.e., reduced order models—based on different homogenization assumptions—isostrain, isostress, and isowork—are used to efficiently and optimally query the materials design space.

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Fig. 1

The stress–strain response of dual-phase phase microstructures with volume fraction of the hard phase, fhard = 0%, 25%, 50%, 75%, and 100%. A 3D representative volume element of the dual-phase microstructure is shown in the inset.

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Fig. 2

Comparison of the variation of the strength normalized strain-hardening rate, (1/τ)(dτ/dεpl) at εpl = 1.5%, with the volume fraction of the hard phase, fhard, as predicted by the three reduced-order models and the microstructure-based finite element model

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Fig. 3

A depiction of total uncertainty, which includes both the uncertainty associated with the Gaussian process and uncertainty associated with the fidelity of the information source

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Fig. 4

Flowchart of the proposed approach

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Fig. 5

The fused model and Gaussian processes of the isowork, isostrain, and isostress models in comparison with the true (RVE) model

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Fig. 6

The fused model and Gaussian processes of the isowork, isostrain, and isostress models in comparison with the true (RVE) model when a few number of data are available in one region

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Fig. 7

The fused model and Gaussian processes of the isowork, isostrain, and isostress models with data in different regions in comparison with the true (RVE) model

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Fig. 8

Number of effective independent information sources, Ieff as a function of fhard for demonstration Case 1

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Fig. 9

The optimal solution obtained by our proposed approach and by applying the knowledge gradient on a GP of only the true data (RVE) for different number of samples queried from the true model

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Fig. 10

The fused model and Gaussian processes of the isowork, isostrain, and isostress models in comparison with the true (RVE) model in iterations 1, 15 and 30

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Fig. 11

Number of samples queried from the true model (RVE) and the information sources in each iteration



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