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Record W4407845866 · doi:10.3390/ma18050941

The Influence of Rare Earth Metals on the Microstructure and Mechanical Properties of 220 and 356.1 Alloys for Automotive Industry

2025· article· en· W4407845866 on OpenAlexaff
H. W. Doty, Shimaa El‐Hadad, E. Isaac Samuel, A. M. Samuel, F. H. Samuel

Bibliographic record

VenueMaterials · 2025
Typearticle
Languageen
FieldEngineering
TopicAluminum Alloys Composites Properties
Canadian institutionsUniversité du Québec à Chicoutimi
Fundersnot available
KeywordsMaterials scienceAlloyMetallurgyMicrostructurePrecipitationDuctility (Earth science)NucleationSupercoolingUltimate tensile strengthRefining (metallurgy)CastingGrain sizePrecipitation hardening

Abstract

fetched live from OpenAlex

Application of rare earths (RE) as grain refiners is well-known in the technology of aluminum alloys for the automotive industry. In the current study, Al-2.4%Cu-0.4%Mg alloy (coded 220) and Al-7.5%Si-0.35%Mg alloy (coded 356.1), were prepared by melting each alloy in a resistance furnace. Strontium (Sr) was used as a modifier, while titanium boride (TiB2) was added as a grain refiner. Measured amounts of Ce and La were added to both alloys (max. 1 wt.%). The alloy melts were poured in a preheated metallic mold. The main part of the study was conducted on tensile testing at room temperature. The results show that although RE would cause grain refining to be about 30–40% through the constitutional undercooling mechanism, grain refining with TiB2 would lead to approximately 90% refining (heterogenous nucleation mechanism). The addition of high purity Ce or La (99.9% purity) has no modification effect regardless of the alloy composition or the concentration of RE. Depending on the alloy ductility, the addition of 0.2 wt.%RE has a hardening effect that causes precipitation of RE in the form of dispersoids (300–700 nm). However, this increase vanishes with the decrease in alloy ductility, i.e., with T6 treatment, due to intensive precipitation of ultra-fine coherent Mg2Si-phase particles. There is no definite distinction in the behavior of Ce or La in terms of their high affinity to interact with other transition elements in the matrix, particularly Ti, Fe, Cu, and Sr. When the melt was properly degassed using high-purity argon and filtered using a 20 ppi ceramic foam filter, prior to pouring the liquid metal into the mold sprue, no measurable number of RE oxides was observed. In conclusion, the application of RE to aluminum castings would only lead to formation of a significant volume fraction of brittle intermetallics. In Ti-free alloys, identification of Ce- or La-intermetallics is doubtful due to the fairly thin thickness of the precipitated platelets (about 1 µm) and the possibility that most of the reported Al, Si, and other elements make the reported values for RE rather ambiguous.

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 categoriesnone
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 score0.004
Threshold uncertainty score0.256

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 science0.0000.000
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 head0.011
GPT teacher head0.203
Teacher spread0.192 · 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 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

Citations7
Published2025
Admission routes1
Has abstractyes

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