Evolution of nanoscale precipitates during common Alloy 718 ageing treatments

Rielli, Vitor Vieira; Theska, Felix; Godor, Flora; Stanojević, Aleksandar; Oberwinkler, Bernd; Primig, Sophie

doi:10.1016/j.matdes.2021.109762

Cited by 20 publications

(10 citation statements)

References 57 publications

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“…We assume that closed objects segmented from niobium iso-composition surfaces (ϕ N b ) represent γ precipitates while closed objects segmented from aluminium plus titanium iso-composition surfaces (ϕ Al+T i ) are γ precipitates. Having made the same assumption in previous work [59,60,133], we continue to use the brown (γ ) and turquoise (γ ) coloring scheme of [59] to distinguish these precipitates.…”

Section: Automated High-throughput Analyses Of Object Ensemblesmentioning

confidence: 99%

“…The dataset was measured, reconstructed, and ranged by Rielli et al [133]. The reconstruction contains a total of 103.2 × 10 6 ions.…”

Section: Automated High-throughput Analyses Of Object Ensemblesmentioning

confidence: 99%

“…Compared to classical analyses it would be necessary that an experimentalist performs 8148 runs via manual, GUIbased analyses and characterizes each object for these runs. This is a highly impractical task already for a single run as most runs have a complex set of triangulated iso-surfaces [60,133].…”

Section: Automated High-throughput Analyses Of Object Ensemblesmentioning

confidence: 99%

“…9c pinpoints that there are also challenges: Namely, one can attribute multiple qualitative types to a given sub-graph: In fact, does object 5410 for instance qualify as a monolith because it does not collide on its neighbors or does it qualify as a quadruplet, or even septet, given that it has six disjoint objects in proximity? We learn that qualitative configuration analyses like those in [60,133] can be formulated more rigorously with our approach.…”

Section: D Characterization Of Co-precipitating Phasesmentioning

confidence: 99%

“…Such analytics yield robust descriptors to qualify which objects are monoliths, duplets, triplets, or arbitrarily complex configurations as the paper reports. Recalling our motivation, paraprobe-intersector delivers an automated alternative to otherwise very tedious manual analyses of coprecipitation phenomena [133] or two-dimensional slicing through images of discretized datasets [59], thus offering rigorous qualification and quantification. Implementation details The above-described tools were implemented in C/C++ and a set of convenience Python tools.…”

Section: Spatial Correlation Analyses -Implementation Detailsmentioning

confidence: 99%

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On Strong-Scaling and Open-Source Tools for High-Throughput Quantification of Material Point Cloud Data: Composition Gradients, Microstructural Object Reconstruction, and Spatial Correlations

Kühbach¹,

Rielli²,

Primig³

et al. 2022

Preprint

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Characterizing microstructure-material-property relations calls for software tools which extract point-cloud-and continuum-scale-based representations of microstructural objects. Application examples include atom probe, electron, and computational microscopy experiments. Mapping between atomic-and continuum-scale representations of microstructural objects results often in representations which are sensitive to parameterization; however assessing this sensitivity is a tedious task in practice.Here, we show how combining methods from computational geometry, collision analyses, and graph analytics yield software tools for automated analyses of point cloud data for reconstruction of three-dimensional objects, characterization of composition profiles, and extraction of multi-parameter correlations via evaluating graph-based relations between sets of meshed objects. Implemented for point clouds with mark data, we discuss use cases in atom probe microscopy that focus on interfaces, precipitates, and coprecipitation phenomena observed in different alloys. The methods are expandable for spatio-temporal analyses of grain fragmentation, crystal growth, or precipitation.

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Section: Automated High-throughput Analyses Of Object Ensemblesmentioning

confidence: 99%

“…The dataset was measured, reconstructed, and ranged by Rielli et al [133]. The reconstruction contains a total of 103.2 × 10 6 ions.…”

Section: Automated High-throughput Analyses Of Object Ensemblesmentioning

confidence: 99%

Section: Automated High-throughput Analyses Of Object Ensemblesmentioning

confidence: 99%

Section: D Characterization Of Co-precipitating Phasesmentioning

confidence: 99%

Section: Spatial Correlation Analyses -Implementation Detailsmentioning

confidence: 99%

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On Strong-Scaling and Open-Source Tools for High-Throughput Quantification of Material Point Cloud Data: Composition Gradients, Microstructural Object Reconstruction, and Spatial Correlations

Kühbach¹,

Rielli²,

Primig³

et al. 2022

Preprint

Self Cite

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Review of Microstructure–Mechanical Property Relationships in Cast and Wrought Ni‐Based Superalloys with Boron, Carbon, and Zirconium Microalloying Additions

et al. 2022

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Cast and wrought Ni‐based superalloys are materials of choice for harsh high‐temperature environments of aircraft engines and gas turbines. Their compositional complexity requires sophisticated thermo‐mechanical processing. A typical microstructure consists of a polycrystalline γ‐matrix, strengthening Ni3(Al,Ti) γ′ precipitates, carbides (MC, M6C, and M23C6), borides (M2B, M3B2, and M5B3), and other inclusions. Microalloying additions of B, C, and Zr commonly improve high‐temperature strength and creep resistance, although excessive additions are detrimental. Grain boundary (GB) segregation may improve cohesion and displace embrittling impurities. Finely dispersed carbides and borides are desired to control grain size via GB pinning. However, excessive decoration of GBs may lead to failure during processing and in‐service. Hence, a systematic review on the roles of B, C, and Zr in cast and wrought Ni‐based superalloys is required. The current state of knowledge on GB segregation and precipitation is reviewed. Experimental and modeling results are compared across various processing steps. The impact of GB precipitation on mechanical properties is most well researched. Co‐precipitation in proximity to GBs interacting with local microstructure evolution and mechanical properties remains less explored. Addressing these gaps in knowledge allows a more complete understanding of processing–microstructure–properties relationships in advanced cast and wrought Ni‐based superalloys.

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