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Solidification Kinetics of an Al-Ce Alloy with Additions of Ni and Mn

2023· preprint· en· W4376116373 on OpenAlexafffund
Jordan Roger Kozakevich, Joshua Stroh, D. Sediako, David Weiss

Bibliographic record

VenuePreprints.org · 2023
Typepreprint
Languageen
FieldEngineering
TopicAluminum Alloy Microstructure Properties
Canadian institutionsOkanagan University CollegeUniversity of British Columbia, Okanagan CampusUniversity of British Columbia
FundersMitacs
KeywordsAlloyEutectic systemMaterials scienceLiquidusMicrostructureMetallurgyCastingPhase (matter)AluminiumCooling curveAnalytical Chemistry (journal)Chemistry

Abstract

fetched live from OpenAlex

Heat-treated aluminum-silicon (Al-Si)-based alloys have dominated the cast lightweight alloy industry for several decades. However, in the last decade, Al-Ce-based alloys have shown promise in replacing Al-Si alloys due to their ability to remove the need for costly heat treatments. Since the properties of Al-Ce alloys depend on the as-cast microstructure, it is important to characterize the solidification kinetics of these alloys. Therefore, this study focused on characterizing the solidification of a near eutectic Al-Ce alloy with additions of Ni and Mn. The alloy was cast in a wedge mold configuration, resulting in cooling rates between 0.18 and 14.27 °C/s. SEM coupled with EDS and DSC techniques characterized the evolution rate of solid phases. The SEM/EDS data revealed that an Al10CeMn2 phase is present at higher cooling rates. At lower cooling rates, near the center of the casting, a proeutectic Al23Ce4Ni6 phase was more present. It was observed that up to 2.6 at. % of Mn was dissolved in this proeutectic Al23Ce4Ni6 phase, thereby removing a large portion of the available Mn for forming the Al10CeMn2 phase. DSC analysis showed differences in the samples' liquidus temperatures which is indicative of compositional variations. Inductively coupled plasma atomic emission spectroscopy (ICP-OES) and Scheil solidification simulations correlated the compositional differences to phase formation, which agreed with the SEM and DSC results. This experimentation provides insight into novel Al-Ce-Ni-Mn alloys and where their potential lies in industrial applications.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.200
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
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 head0.078
GPT teacher head0.287
Teacher spread0.209 · 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.

Study designBench or experimental
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

Citations5
Published2023
Admission routes2
Has abstractyes

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