Understanding creep of a single-crystalline Co-Al-W-Ta superalloy by studying the deformation mechanism, segregation tendency and stacking fault energy

Volz, Nicklas; Xue, Fei; Zenk, Christopher H.; Bezold, Alexander; Gabel, Stefan; Subramanyam, Aparna P. A.; Drautz, Ralf; Hammerschmidt, Thomas; Makineni, Surendra Kumar; Gault, Baptiste; Göken, Mathias; Neumeier, Steffen

doi:10.1016/j.actamat.2021.117019

Cited by 32 publications

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“…A similar impact of diffusion-assisted shearing of the γ phase by partial dislocations on the creep behavior of a similar Co-Al-W-Ta alloy was recently found by Volz et al [25]. There, the intermediate softening due to the cutting of γ was also shifted to shorter test times at higher temperatures, since the faster segregation of Ta and W to dislocations and stacking faults facilitated the shearing processes.…”

Section: Discussionsupporting

confidence: 78%

“…The increase in dislocation density in the γ matrix leads to an impediment in the movement of matrix dislocations. Presumably, the segregation of alloying elements to the dislocations located at the γ/γ interface facilitates cutting into the γ phase, resulting in a significant increase in stacking fault density [11,25,26]. The interaction of stacking faults with each other can then lead to a significant increase in strength [11,[15][16][17][18].…”

Section: Discussionmentioning

confidence: 99%

“…Furthermore, extensive stacking fault formation and frequent interactions between these planar defects also cause high work hardening rates [3,11] and this is also assumed to cause strengthening during creep [13][14][15][16][17][18]. Since stacking fault shearing is a segregation-assisted process [14,[19][20][21][22][23][24][25][26], these strengthening effects, however, are diminished if the temperature becomes too high as their propagation velocity increases and extrinsic stacking faults evolve into microtwins.…”

Section: Introductionmentioning

confidence: 99%

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Anomalous Work Hardening Behavior of a Single Crystalline Co-Base Superalloy

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The defect evolution associated with an anomalous work hardening behavior of a single crystalline quaternary Co-Al-W-Ta superalloy at 950 °C was investigated by transmission electron microscopy. As plastic deformation is initially confined to the γ matrix channels, a plateau arises in the stress-strain curve after yielding. At about 1% plastic strain, extensive shearing of the γ′ precipitates under superlattice stacking fault formation occurs leading to extreme work hardening rates up to 12 GPa and a total increase in stress of about 200 MPa. Additional investigations on the temperature and strain-rate dependence of the anomalous work hardening behavior reveal the significance of diffusion and segregation processes on the stress-strain curve and the work hardening behavior.

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Section: Discussionsupporting

confidence: 78%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

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Anomalous Work Hardening Behavior of a Single Crystalline Co-Base Superalloy

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et al. 2022

Alloys

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“…Taking mechanical deformation as example, it is known that the modification of stacking-fault energies by alloying elements affects the macroscopic plastic deformation, see e.g. reference [18]. However, it is not clear at the moment how to join data of electronic-structure calculations of stacking-fault energies with experimental stress-strain data in order to machine-learn the influence of chemical composition on the mechanical strength of a microstructured material.…”

Section: Current and Future Challengesmentioning

confidence: 99%

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In recent years, we have been witnessing a paradigm shift in computational materials science. In fact, traditional methods, mostly developed in the second half of the XXth century, are being complemented, extended, and sometimes even completely replaced by faster, simpler, and often more accurate approaches. The new approaches, that we collectively label by machine learning, have their origins in the fields of informatics and artificial intelligence, but are making rapid inroads in all other branches of science. With this in mind, this Roadmap article, consisting of multiple contributions from experts across the field, discusses the use of machine learning in materials science, and share perspectives on current and future challenges in problems as diverse as the prediction of materials properties, the construction of force-fields, the development of exchange correlation functionals for density-functional theory, the solution of the many-body problem, and more. In spite of the already numerous and exciting success stories, we are just at the beginning of a long path that will reshape materials science for the many challenges of the XXIth century.

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“…The number of techniques available to identify, quantify, and characterize extended defects has also grown beyond ex-situ S/TEM and atomic force microscopy (AFM) [68] to include atom probe tomography [93] and in-situ TEM [94]. The increased availability of in-situ holders allows for more studies on the exsolution process itself, particularly allowing for pinpointing the influence of temperature ramp on the growth mechanism.…”

Section: Advances In Science and Technology To Meet Challengesmentioning

confidence: 99%

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Over the last decade, exsolution has emerged as a powerful new method for decorating oxide supports with uniformly dispersed nanoparticles for energy and catalytic applications. Due to their exceptional anchorage, resilience to various degradation mechanisms, as well as numerous ways in which they can be produced, transformed and applied, exsolved nanoparticles have set new standards for nanoparticles in terms of activity, durability and functionality. In conjunction with multifunctional supports such as perovskite oxides, exsolution becomes a powerful platform for the design of advanced energy materials. In the following sections, we review the current status of the exsolution approach, seeking to facilitate transfer of ideas between different fields of application. We also explore future directions of research, particularly noting the multi-scale development required to take the concept forward, from fundamentals through operando studies to pilot scale demonstrations.

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Understanding creep of a single-crystalline Co-Al-W-Ta superalloy by studying the deformation mechanism, segregation tendency and stacking fault energy

Cited by 32 publications

References 63 publications

Anomalous Work Hardening Behavior of a Single Crystalline Co-Base Superalloy

Anomalous Work Hardening Behavior of a Single Crystalline Co-Base Superalloy

Roadmap on Machine learning in electronic structure

Roadmap on exsolution for energy applications

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