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An investigation of the chill-casting interface dynamics in production of sand-cast A319 engine blocks

2019· article· en· W2947159354 on OpenAlexaff
Farzaneh Farhang Mehr, Steve Cockcroft, Daan M. Maijer

Bibliographic record

VenueIOP Conference Series Materials Science and Engineering · 2019
Typearticle
Languageen
FieldEngineering
TopicAluminum Alloy Microstructure Properties
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsDurabilityThermocoupleCastingCylinder blockAutomotive industryAutomotive engineMechanical engineeringMaterials scienceEngineeringCylinderMetallurgyComposite material

Abstract

fetched live from OpenAlex

Abstract In recent years, the automotive industry has been increasing the production of small, high-power gas engines as part of several strategies to achieve the new “Corporate Average Fuel Economy” (CAFE) standards. This trend requires an improvement in the thermal and mechanical fatigue durability of the aluminium alloys used in the production of the cylinder blocks in these engines. Conventionally, solid chills are employed in areas of these castings subject to high cyclic loading to enhance the mechanical performance of the cast material – i.e. in the main bearing bulkhead. A potential means of improving the efficacy of these chills is to incorporate water cooling. To assess the effectiveness of this method, a water-cooled chill was designed and installed in a bonded-sand engine block mould package (1/4 section). The moulds were instrumented with thermocouples, to measure the evolution of temperature at key locations in the casting, and “Linear Variable Displacement Transducers” (LVDTs), to measure the gap formation at the interface between the chill and the casting. This paper summarizes, at a high level, some of the findings of this work.

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 score0.012
Threshold uncertainty score0.494

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 communication0.0000.001
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 head0.008
GPT teacher head0.187
Teacher spread0.179 · 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

Citations0
Published2019
Admission routes1
Has abstractyes

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