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Recovery of Aluminum Alloy A380 from Machining Chips

2016· article· en· W2372212941 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

VenueApplied Mechanics and Materials · 2016
Typearticle
Languageen
FieldEngineering
TopicAdvanced Machining and Optimization Techniques
Canadian institutionsUniversity of Windsor
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of Windsor
KeywordsTaguchi methodsOrthogonal arrayMaterials scienceAlloyFlux (metallurgy)AluminiumMetallurgyMachiningRecovery rateRefining (metallurgy)Composite materialChromatographyChemistry

Abstract

fetched live from OpenAlex

Die casting aluminum alloy A380 were recoveredfrom high pressure die cast machining chipsunder a series of designed experiments using Taguchi Method, where flux types (FT), chips/flux ratio (CFR), holding times (Ht) and temperatures (HT) were selected as four factors. For each factor, three levels were chosen to create Taguchi orthogonal array. The recovery rate (R r ) was selected as a response to evaluate the effectiveness of the recovery process. An analysis of the mean of noise-to-signal (S/N) ratios indicates that the recovery rate is affected considerably by the levels in the Taguchi orthogonal array. The optimum combination leads to the highest recovery rate of 92.03% by using Al-clean 101 as the refining flux, 10:5 as the chips/flux ratio, 60 minutes and 760°Cas the holding time and holding temperature.

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: none
Teacher disagreement score0.836
Threshold uncertainty score0.372

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 head0.007
GPT teacher head0.193
Teacher spread0.186 · 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