CALPHAD-informed phase-field modeling of grain boundary microchemistry and precipitation in Al-Zn-Mg-Cu alloys

Liu, Chuanlai; Garner, Alistair; Zhao, Huan; Prangnell, P.B.; Gault, Baptiste; Raabe, Dierk; Shanthraj, Pratheek

doi:10.1016/j.actamat.2021.116966

Cited by 31 publications

(3 citation statements)

References 64 publications

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“…Their nonequilibrium multi-phase field model (MPFM) coupled with the CALPHAD database predicted cell sizes that were in agreement with experimental data. The approaches mentioned above are generally limited to simple binary and ternary systems, as increasing the number of components rapidly escalates the computational complexity [51]. Therefore, in a recent study, Liu et al [51] introduced a time-discrete semi-analytical inversion of the thermodynamic relations applicable to general forms of the Gibbs free energy.…”

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

confidence: 99%

“…The approaches mentioned above are generally limited to simple binary and ternary systems, as increasing the number of components rapidly escalates the computational complexity [51]. Therefore, in a recent study, Liu et al [51] introduced a time-discrete semi-analytical inversion of the thermodynamic relations applicable to general forms of the Gibbs free energy. Linking it with a CALPHAD database enabled the authors to study the grain boundary precipitation and microchemistry evolution in a quaternary multi-component Al-Zn-Mg-Cu system, at length and time scales relevant to typical industrial heat treatment processes.…”

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: Modelling Of Nonequilibrium Microstructures Encountered In Am: Recent Progressmentioning

confidence: 99%

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

confidence: 99%

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

Gunasegaram

Steinbach

2021

Metals

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“…Al-Cu alloys have applications in structural, aerospace, marine, and automotive industries for their high strength [13,14] . Due to the high strength obtained by fine-scale precipitation of the metastable zinc-rich phase in the hardening heat treatment process, Al-Zn alloys are widely used in aircraft manufacturing and other high-strength applications [15][16][17] .…”

Section: Introductionmentioning

confidence: 99%

Boosting for concept design of casting aluminum alloys driven by combining computational thermodynamics and machine learning techniques

Wang¹,

Liu²,

Gao³

et al. 2021

JMI

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Casting aluminum alloys are commonly used in industries due to their excellent comprehensive performance. Alloying/microalloying and post-solidification heat treatments are the most common measures to tune the microstructure for enhancing their mechanical properties. However, it is very challenging to achieve accurate and efficient development of novel casting aluminum alloys using the traditional trial-and-error method. With the rapid development of computer technology, the computational thermodynamics (CT) in the framework of the CALculation of PHAse Diagram approach, the data-driven machine learning (ML) technique, and also their combinations have been proved to be effective approaches for the design of casting aluminum alloys. In this review, the state-of-the-art computational alloy design approaches driven by CT and ML techniques, as well as their combinations, were comprehensively summarized. The current status of the thermodynamic database for aluminum alloys, as the core for CT, was also briefly introduced. After that, a variety of successful case studies on the design of different casting aluminum alloys driven by CT, ML, and their combinations were demonstrated, including common applications, CT-driven design of Sc-additional Al-Si-Mg series casting alloys, and design of Srmodified A356 alloys driven by combing CT and ML. Finally, the conclusions of this review were drawn, and perspectives for boosting the computational design approach driven by combining CT and ML techniques were pointed out.

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Growth modes of grain boundary precipitate in aluminum alloys under different lattice misfits

et al. 2022

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CALPHAD-informed phase-field modeling of grain boundary microchemistry and precipitation in Al-Zn-Mg-Cu alloys

Cited by 31 publications

References 64 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

Boosting for concept design of casting aluminum alloys driven by combining computational thermodynamics and machine learning techniques

Growth modes of grain boundary precipitate in aluminum alloys under different lattice misfits

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