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Fatigue life improvement of cast ZK60 Mg alloy through low temperature closed-die forging for automotive applications

2018· article· en· W2804077194 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

VenueMATEC Web of Conferences · 2018
Typearticle
Languageen
FieldMaterials Science
TopicMagnesium Alloys: Properties and Applications
Canadian institutionsNatural Resources CanadaUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsForgingMaterials scienceAlloyMetallurgyDie (integrated circuit)Texture (cosmology)Magnesium alloyGrain sizeComposite materialComputer science

Abstract

fetched live from OpenAlex

The influence of low-temperature closed-die forging on the quasi-static and cyclic behaviour of as-cast ZK60 magnesium alloy was investigated. As-cast ZK60 billets were forged at a ram speed of 20 mm/sec and a temperature of 250 °C. While the yield strength of the starting alloy was 139 MPa, the forging process improved the yield strength significantly by ~68% to 234 MPa. Moreover, the stresscontrolled push-pull fatigue tests at the stress amplitudes of 140 MPa to 180 MPa revealed that the fatigue life was enhanced by an order of magnitude. Microstructural analyses besides the texture measurements at different locations of the forged part manifested partial grain refinement and texture modification strengthening mechanisms. It is believed that the fatigue life improvement is achieved in the wake of the grain refinement and the subsequent material strengthening.

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 score0.032
Threshold uncertainty score0.758

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)0.0010.000

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 head0.031
GPT teacher head0.281
Teacher spread0.250 · 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