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Record W2003101689 · doi:10.4271/2014-01-0837

Residual Stress Mapping along the Cylinder Bores of Al Alloy Engine Blocks Subjected to Production Solution Heat Treatment Schedule

2014· article· en· W2003101689 on OpenAlexafffund
Anthony Lombardi, D. Sediako, C. Ravindran, Robert Mackay

Bibliographic record

VenueSAE International Journal of Materials and Manufacturing · 2014
Typearticle
Languageen
FieldEngineering
TopicWelding Techniques and Residual Stresses
Canadian institutionsNational Research Council CanadaToronto Metropolitan University
FundersAUTO21 Network of Centres of Excellence
KeywordsResidual stressAlloyMaterials scienceScheduleCylinderStructural engineeringStress (linguistics)EngineeringComposite materialMechanical engineeringComputer science

Abstract

fetched live from OpenAlex

<div class="section abstract"><div class="htmlview paragraph">The development of an optimized heat treatment schedule, with the aim of maximizing strength and relieving tensile residual stress, is important to prevent in-service cylinder distortion in Al alloy engine blocks containing cast-in gray iron liners. However, to effectively optimize the engine block heat treatment schedule, the current solutionizing parameters must be analyzed and compared to the as-cast condition to establish a baseline for residual stress relief. In this study, neutron diffraction was carried out to measure the residual stress along the aluminum cylinder bridge following solution heat treatment. The stresses were measured in the hoop, radial and axial orientations and compared to a previous measured as-cast (TSR) engine block. The results suggest that solution heat treatment using the current production parameters partially relieved tensile residual stress in the Al cylinder bridge, with stress relief being more effective near the bottom of the cylinder.</div></div>

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 score0.072
Threshold uncertainty score0.351

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 head0.233
Teacher spread0.222 · 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

Citations15
Published2014
Admission routes2
Has abstractyes

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