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Record W4407095464 · doi:10.1016/j.jalmes.2025.100159

Metallurgical assessment of Al-Zr-Y alloys for laser-based processing

2025· article· en· W4407095464 on OpenAlexafffund
Jonathan Hierlihy, I.W. Donaldson, D.P. Bishop

Bibliographic record

VenueJournal of Alloys and Metallurgical Systems · 2025
Typearticle
Languageen
FieldEngineering
TopicAdditive Manufacturing Materials and Processes
Canadian institutionsDalhousie University
FundersNatural Sciences and Engineering Research Council of CanadaResearch Nova Scotia
KeywordsMetallurgyMaterials scienceLaserMaterials processingEngineeringManufacturing engineeringOpticsPhysics

Abstract

fetched live from OpenAlex

The scope of aluminum alloys commercially available for laser-based additive manufacturing is limited yet the demand for them is growing aggressively. In many cases, end-users are particularly interested in those that offer enhanced thermal stability. Historically, several such materials were premised on alloys that incorporated transition metal (TM) additions which formed refractory aluminides as the principal strengthening addition. The objective of this study was to pursue a similar concept but as applied to the ternary Al-Zr-Y alloy system . In doing so, plates with varying Zr and Y contents (0–2 wt%) were cast and subsequently subjected to laser remelting (LRM) using a Yb-fibre laser. Microstructures then characterized using laser confocal microscopy , XRD , SEM, and TEM . LRM was seen to produce an epitaxial columnar α-Al matrix in binary Al-Y alloys, with intergranular solidification cracking seen in the highest Y content of 2 wt%. In Al-Zr specimens, increasing Zr content resulted in the development of a duplex microstructure consisting of distinct epitaxial columnar regions near the melt pool boundary and equiaxed regions near the center. The development of equiaxed regions was ascribed to the presence of sub-micron dispersoids . These dispersoids were Zr-rich and increased in number with corresponding increases in Zr content. They were also observed in Al-Zr-Y specimens and were subsequently identified as an L1 2 -Al 3 Zr. The addition of Y produced a dramatic increase in dispersoid density, and consequently the proportion of equiaxed grains, demonstrating that the Al-Zr-Y system is a promising candidate for laser-based processing technologies.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.654
Threshold uncertainty score0.671

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.014
GPT teacher head0.278
Teacher spread0.264 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations1
Published2025
Admission routes2
Has abstractyes

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