Proceedings of the 4th World Congress on Integrated Computational Materials Engineering (ICME 2017)

Mason, Paul; Strachan, Alejandro; Glamm, Ryan J.; Singh, Amarendra K.; Schmitz, Georg J.; Fisher, Charles R.; Manuel, Michele V.

doi:10.1007/978-3-319-57864-4

Cited by 5 publications

(8 citation statements)

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“…At this length scale, a coarse grained representation of the bainitic ferrite sheaves is necessary for the balance between representative volume element (RVE) size and computational costs of the model. Therefore, in [74][75][76], we have described every bainitic packet (sheaves of the same close-packed group) by individual order parameters. The mechanical and chemical driving forces are derived as variational derivative of the free energy functional.…”

Section: Phenomenological Phase Field Description Of Bainitic Sheaf Packetsmentioning

confidence: 99%

“…Using the multi-phase-field framework of Steinbach et al [77], different interfacial properties can be assigned to the austenite-austenite grain boundaries, austenite-packet boundaries and the inter-packet boundaries. In [74,76], phase field simulations have been coupled with a finite element simulation on the macroscopic scale. For each integration point of the finite elements, an individual phase field simulation domain is created, where the stress tensor of the integration point is incrementally applied to the phase field simulation.…”

Section: Phenomenological Phase Field Description Of Bainitic Sheaf Packetsmentioning

confidence: 99%

“…The simulation can reproduce the overall transformation induced strain and the effect of variant selection on bainitic packets. For further details of the simulation approach and results, we refer to [74].…”

Section: Phenomenological Phase Field Description Of Bainitic Sheaf Packetsmentioning

confidence: 99%

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Modeling Bainitic Transformations during Press Hardening

et al. 2021

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We revisit recent findings on experimental and modeling investigations of bainitic transformations under the influence of external stresses and pre-strain during the press hardening process. Experimentally, the transformation kinetics in 22MnB5 under various tensile stresses are studied both on the macroscopic and microstructural level. In the bainitic microstructure, the variant selection effect is analyzed with an optimized prior-austenite grain reconstruction technique. The resulting observations are expressed phenomenologically using a autocatalytic transformation model, which serves for further scale bridging descriptions of the underlying thermo-chemo-mechanical coupling processes during the bainitic transformation. Using analyses of orientation relationships, thermodynamically consistent and nondiagonal phase field models are developed, which are supported by ab initio generated mechanical parameters. Applications are related to the microstructure evolution on the sheaf, subunit, precipitate and grain boundary level.

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Section: Phenomenological Phase Field Description Of Bainitic Sheaf Packetsmentioning

confidence: 99%

Section: Phenomenological Phase Field Description Of Bainitic Sheaf Packetsmentioning

confidence: 99%

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Modeling Bainitic Transformations during Press Hardening

et al. 2021

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“…This leads to decreased calculation time compared with MPFM. We have developed a CALPHAD database-coupled NEMPFM program for a practical engineering steel system using TQ-interface [7].…”

Section: Introductionmentioning

confidence: 99%

“…Our CALPHAD database coupling NEMPFM using TQ-Interface is similar to their procedure. Furthermore, we constructed a specific diffusion potential formulation for a quinary sublattice CALPHAD model containing carbon as the interstitial element [7]. Nevertheless, there is a difference between our method and theirs.…”

Section: Introductionmentioning

confidence: 99%

Solidification analysis by non-equilibrium phase field model using thermodynamics data estimated by machine learning

Nomoto

Wakameda²,

Segawa³

et al. 2019

Modelling Simul. Mater. Sci. Eng.

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A multi-phase field method (MPFM) using the finite interface dissipation model is applied to simulate the solidification microstructure evolution of a stainless-steel composition, including the delta-ferrite to gamma austenite peritectic transformation. The calculation is performed for a quinary system of engineering steel in a two-dimensional field. Thermodynamics calculations using the CALPHAD database in this MPFM are replaced by machine learning prediction to reduce the numerical time. Neural network methodology is introduced for machine learning in this study. The Gibbs free energy and chemical potential values estimated from the CALPHAD database coupling results are inputted into the neural network learning procedure, together with the composition and temperature values. The microstructure evaluated using the obtained neural network parameter is in good agreement with that directly coupled with the CALPHAD database. This calculation is approximately five times faster than direct CALPHAD calculation.

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A Perspective on Digital Knowledge Representation in Materials Science and Engineering

et al. 2022

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In this perspective article, we present the project initiative Materials-open-Laboratory (Mat-o-Lab) that aims to provide a collaborative environment for domain experts to digitize their research results and processes and make them fit for data-driven materials research and development. The overarching challenge is to generate connection points to further link data from other domains to harness the promised potential of big materials data and harvest new knowledge.

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Proceedings of the 4th World Congress on Integrated Computational Materials Engineering (ICME 2017)

Cited by 5 publications

References 0 publications

Modeling Bainitic Transformations during Press Hardening

Modeling Bainitic Transformations during Press Hardening

Solidification analysis by non-equilibrium phase field model using thermodynamics data estimated by machine learning

A Perspective on Digital Knowledge Representation in Materials Science and Engineering

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