Quantitative phase-field modeling of solute trapping in rapid solidification

Kavousi, Sepideh; Zaeem, Mohsen Asle

doi:10.1016/j.actamat.2020.116562

Cited by 40 publications

(18 citation statements)

References 42 publications

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“…Several numerical methods are capable of simulating microstructures in AM at the required level of resolution, i.e., at the mesoscopic scale (that spans from nanometres to micrometres) and at the continuum scale. Some examples of these are front-tracking [28], phase field (PF) [12,19,[29][30][31], level set (LS) [32], lattice Boltzmann (LB) [30,33], Potts kinetic Monte Carlo (kMC) [12,19,30,34], cellular automata [12,19,29,30] and the Johnson-Mehl-Avrami-Kolmogorov (JMAK) theory-based phenomenological model [35,36]. These varied techniques have unique strengths and weaknesses [12,19,30] which means modellers can select the method best suited to their tasks based on these considerations.…”

Section: A Summary Of Modelling Methodsmentioning

confidence: 99%

“…This is because the solute atoms are unable to diffuse ahead of the fast-moving liquid-solid interface, and the concentration exceeds the solubility limit. Several models have been advanced to describe this experimentally observed phenomenon [31]. Recently, purely quantitative phase field models have been developed from fundamental considerations for predicting solute trapping at velocities relevant to AM, e.g., [31] for the Si-9at.%As system.…”

Section: Modelling Of Nonequilibrium Microstructures Encountered In Am: Recent Progressmentioning

confidence: 99%

“…Several models have been advanced to describe this experimentally observed phenomenon [31]. Recently, purely quantitative phase field models have been developed from fundamental considerations for predicting solute trapping at velocities relevant to AM, e.g., [31] for the Si-9at.%As system. Similar numerical models are required for other alloys used in AM to simulate microstructure formation accurately.…”

Section: Modelling Of Nonequilibrium Microstructures Encountered In Am: Recent Progressmentioning

confidence: 99%

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Modelling of Microstructure Formation in Metal Additive Manufacturing: Recent Progress, Research Gaps and Perspectives

Gunasegaram

Steinbach

2021

Metals

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Microstructures encountered in the various metal additive manufacturing (AM) processes are unique because these form under rapid solidification conditions not frequently experienced elsewhere. Some of these highly nonequilibrium microstructures are subject to self-tempering or even forced to undergo recrystallisation when extra energy is supplied in the form of heat as adjacent layers are deposited. Further complexity arises from the fact that the same microstructure may be attained via more than one route—since many permutations and combinations available in terms of AM process parameters give rise to multiple phase transformation pathways. There are additional difficulties in obtaining insights into the underlying phenomena. For instance, the unstable, rapid and dynamic nature of the powder-based AM processes and the microscopic scale of the melt pool behaviour make it difficult to gather crucial information through in-situ observations of the process. Therefore, it is unsurprising that many of the mechanisms responsible for the final microstructures—including defects—found in AM parts are yet to be fully understood. Fortunately, however, computational modelling provides a means for recreating these processes in the virtual domain for testing theories—thereby discovering and rationalising the potential influences of various process parameters on microstructure formation mechanisms. In what is expected to be fertile ground for research and development for some time to come, modelling and experimental efforts that go hand in glove are likely to provide the fastest route to uncovering the unique and complex physical phenomena that determine metal AM microstructures. In this short Editorial, we summarise the status quo and identify research opportunities for modelling microstructures in AM. The vital role that will be played by machine learning (ML) models is also discussed.

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Section: A Summary Of Modelling Methodsmentioning

confidence: 99%

Section: Modelling Of Nonequilibrium Microstructures Encountered In Am: Recent Progressmentioning

confidence: 99%

Section: Modelling Of Nonequilibrium Microstructures Encountered In Am: Recent Progressmentioning

confidence: 99%

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Modelling of Microstructure Formation in Metal Additive Manufacturing: Recent Progress, Research Gaps and Perspectives

Gunasegaram

Steinbach

2021

Metals

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“…Moreover, the solidification time is significantly shortened compared to the low ∆T. As a result, the diffusion of the atoms is largely weakened, which causes the effect of solute trapping, with no deviation in the composition [37]. In our previous work, the FCC-Co and Co 3 B phase grows faster than that in the low ∆T, at approximately 1.4 m/s and 0.8 m/s [13], respectively, which increases the partition coefficient, K V , and the liquid and solid compositions could be similar.…”

Section: Formation Of Fcc-co/co 3 B Anomalous Eutectic With High ∆Tmentioning

confidence: 99%

Re-Examination of the Microstructural Evolution in Undercooled Co-18.5at.%B Eutectic Alloy

et al. 2022

Materials

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The undercooling (∆T) dependencies of the solidification pathways, microstructural evolution, and recalescence behaviors of undercooled Co-18.5at.%B eutectic alloys were systematically explored. Up to four possible solidification pathways were identified: (1) A lamellar eutectic structure consisting of the FCC–Co and Co3B phase forms, with extremely low ΔT; (2) The FCC–Co phase primarily forms, followed by the eutectic growth of the FCC–Co and Co2B phases when ΔT < 100 K; (3) As the ΔT increases further, the FCC–Co phase primarily forms, followed by the metastable Co23B6 phase with the trace of an FCC–Co and Co23B6 eutectic; (4) When the ΔT increases to 277 K, the FCC–Co phase primarily forms, followed by an FCC–Co and Co3B eutectic, which is similar in composition to the microstructure formed with low ΔT. The mechanisms of the microstructural evolution and the phase selection are interpreted on the basis of the composition segregation, the skewed coupled zone, the strain-induced transformation, and the solute trapping. Moreover, the prenucleation of the primary FCC–Co phase was also detected from an analysis of the different recalescence behaviors. The present work not only enriches our knowledge about the phase selection behavior in the undercooled Co–B system, but also provides us with guidance for controlling the microstructures and properties practically.

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“…Alternative methods include using a relaxation equation for the partition coefficient [368], or prescribing interface conditions through thermodynamic extremal principle [369]. Recently, a pragmatic approach was also promposed that consists in modifying the conventional anti-trapping current term [218], performing a thin-interface asymptotic analysis on the resulting equations, and calibrating the parame-ters of the modified anti-trapping term to an analytical rapid solidification model, typically using the classical continuous growth model [370,371].…”

Section: Rapid Solidificationmentioning

confidence: 99%

Phase-field modeling of microstructure evolution: Recent applications, perspectives and challenges

Tourret¹,

Liu²,

Llorca³

2021

Preprint

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We briefly review the state-of-the-art in phase-field modeling of microstructure evolution. The focus is placed on recent applications of phase-field simulations of solid-state microstructure evolution and solidification that have been compared and/or validated with experiments. They show the potential of phase-field modeling to make quantitative predictions of the link between processing and microstructure. Finally, some current challenges in extending the application of phase-field models within the context of integrated computational materials engineering are mentioned.

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Quantitative phase-field modeling of solute trapping in rapid solidification

Cited by 40 publications

References 42 publications

Modelling of Microstructure Formation in Metal Additive Manufacturing: Recent Progress, Research Gaps and Perspectives

Modelling of Microstructure Formation in Metal Additive Manufacturing: Recent Progress, Research Gaps and Perspectives

Re-Examination of the Microstructural Evolution in Undercooled Co-18.5at.%B Eutectic Alloy

Phase-field modeling of microstructure evolution: Recent applications, perspectives and challenges

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