Finite Interface Dissipation Phase Field Modeling of Ni-Nb Under Additive Manufacturing Conditions

Karayagiza, Kubra; Johnson, Luke; Seede, Raiyan; Attari, Vahid; Zhang, Bing; Huang, Xueqin; Ghosh, Supriyo; Duong, Thien; Караман, И.; Elwany, Alaa; Arróyave, Raymundo

doi:10.2139/ssrn.3406951

Cited by 3 publications

(1 citation statement)

References 52 publications

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“…Similarly to the case of CALPHAD-based thermodynamic as-sessments, the application of UQ/UP frameworks to phase-field modeling remains relatively unexplored [26][27][28][29][30][31]. Koslowski et al [15] characterized how uncertainties propagate across spatial and temporal scales in a physics-based model of nanocrystalline plasticity of fcc metals, combining molecular dynamics (MD) with phase field dislocation dynamics (PFDD) simulations.…”

Section: Introductionmentioning

confidence: 99%

Uncertainty propagation in a multiscale CALPHAD-reinforced elastochemical phase-field model

et al. 2020

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ICME approaches provide decision support for materials design by establishing quantitative process-structure-property relations. Confidence in the decision support, however, must be achieved by establishing uncertainty bounds in ICME model chains. The quantification and propagation of uncertainty in computational materials science, however, remains a rather unexplored aspect of computational materials science approaches. Moreover, traditional uncertainty propagation frameworks tend to be limited in cases with computationally expensive simulations. A rather common and important model chain is that of CALPHAD-based thermodynamic models of phase stability coupled to phase field models for microstructure evolution. Propagation of uncertainty in these cases is challenging not only due to the sheer computational cost of the simulations but also because of the high dimensionality of the input space. In this work, we present a framework for the quantification and propagation of uncertainty in a CALPHADbased elasto-chemical phase field model. We motivate our work by investigating the microstructure evolution in Mg 2 (Si x Sn 1−x ) thermoelectric materials. We first carry out a Markov Chain Monte Carlo-based inference of the CALPHAD model parameters for this pseudobinary system and then use advanced sampling schemes to propagate uncertainties across a high-dimensional simulation input space. Through high-throughput phase field simulations we generate 200,000 time series of synthetic microstructures and use machine learning approaches to understand the effects of propagated uncertainties on the microstructure landscape of the system under study. The microstructure dataset has been curated in the Open Phase-field Microstructure Database (OPMD), available at http://microstructures.net.

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Section: Introductionmentioning

confidence: 99%

Uncertainty propagation in a multiscale CALPHAD-reinforced elastochemical phase-field model

et al. 2020

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Uncertainty Quantification and Propagation in Computational Materials Science and Simulation-Assisted Materials Design

Honarmandi

Arróyave

2020

Integr Mater Manuf Innov

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Computational modelling of process–structure–property–performance relationships in metal additive manufacturing: a review

Hashemi

Parvizi

Baghbanijavid

et al. 2021

International Materials Reviews

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In the current review, an exceptional view on the multi-scale integrated computational modelling and data-driven methods in the Additive manufacturing (AM) of metallic materials in the framework of integrated computational materials engineering (ICME) is discussed. In the first part of the review, process simulation (P-S linkage), structure modelling (S-P linkage), property simulation (S-P linkage), and integrated modelling (PSP and PSPP linkages) are elaborated considering different physical phenomena (multi-physics) in AM and at micro/meso/macro scales (multi-scale modelling). The second part provides an extensive discussion of a data-driven framework, which involves extracting existing data from databases and texts, data pre-processing, high throughput screening, and, therefore, database construction. A data-driven workflow that integrates statistical methods, including ML, artificial intelligence (AI), and neural network (NN) models, has great potential for completing PSPP linkages. This review paper provides an insight for both academic and industrial researchers, working on the AM of metallic materials.

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Finite Interface Dissipation Phase Field Modeling of Ni-Nb Under Additive Manufacturing Conditions

Cited by 3 publications

References 52 publications

Uncertainty propagation in a multiscale CALPHAD-reinforced elastochemical phase-field model

Uncertainty propagation in a multiscale CALPHAD-reinforced elastochemical phase-field model

Uncertainty Quantification and Propagation in Computational Materials Science and Simulation-Assisted Materials Design

Computational modelling of process–structure–property–performance relationships in metal additive manufacturing: a review

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