Integrated Computational Materials Engineering (ICME) Multi-Scale Model Development for Advanced High Strength Steels

Savic, Vesna; Hector, Louis G.; Basu, Ushnish; Basudhar, Anirban; Gandikota, Imtiaz; Stander, Nielen; Park, Taejoon; Pourboghrat, Farhang; Choi, Kyoo Sil; X, Sun; Hu, Jun; Abu-Farha, Fadi; Kumar, Satish

doi:10.4271/2017-01-0226

Cited by 4 publications

(1 citation statement)

References 13 publications

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“…Explicit integration of multiple tools is technically challenging, particularly because of the considerable expense of computational models, the complexity of the input/output interfaces of such models, and the asynchronous nature of the development of such tools. We would like to note, however, that some efforts have recently emerged that attempt to explicitly integrate models within a single framework for materials design [10,11,12]. Approaches that instead use statistical techniques and machine learning tools to better sample the design space have proven to be effective [13].…”

Section: Challenges and Opportunitiesmentioning

confidence: 99%

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

Ghoreishi

Molkeri

Srivastava

et al. 2018

Journal of Mechanical Design

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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 valuegradient 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 * Address all correspondence to this author.the current state. The framework is demonstrated on the optimization of a dual-phase steel to maximize its strengthnormalized 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 assumptionsisostrain, isostress and isowork-are used to efficiently and optimally query the materials design space. arXiv:1809.09686v1 [physics.app-ph]

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