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Record W4408981910 · doi:10.4236/msce.2025.133005

Optimization of Strengths and Electrical Conductivities of Al-Si-Cu-Ni-Sr Alloys

2025· article· en· W4408981910 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueJournal of Materials Science and Chemical Engineering · 2025
Typearticle
Languageen
FieldEngineering
TopicAluminum Alloy Microstructure Properties
Canadian institutionsUniversity of Windsor
FundersUniversity of WindsorFord Motor Company
KeywordsMaterials scienceMetallurgyElectrical resistivity and conductivityElectrical engineering

Abstract

fetched live from OpenAlex

The aim of this study is to develop new cast aluminum alloys for the production of rotor bar in the rotor with high as-cast strength and electrical conductivity. A design of experiment (DOE) technique, Taguchi method, was used to develop high as-cast strength and electrical conductivity alloys with various element addition of Si, Cu, Ni and Sr. The optimal combination of chemical composition for maximizing the ultimate tensile strength (UTS), electrical conductivity (σ) and yield strength (YS) was 6 wt.% Si, 3 wt.% Cu, 0.03 wt.% Sr and 0.5 wt.% Ni. The alloy with the optimal composition had an averaged UTS of 247.58 MPa, an averaged electrical conductivity is 38.01%IACS, and an averaged yield strength is 143.47 Mpa.

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.

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 score0.339

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 head0.004
GPT teacher head0.201
Teacher spread0.197 · 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