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Record W4241023098 · doi:10.32920/ryerson.14648349

A Study of Cylinder Bore Distortion in V6 Aluminum Alloy Engine Blocks

2021· preprint· en· W4241023098 on OpenAlexaff
Anthony Lombardi

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEngineering
TopicMechanical Failure Analysis and Simulation
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsCylinder blockMicrostructureResidual stressMaterials scienceCylinderAlloyUltimate tensile strengthMetallurgyCylinder headAluminiumComposite materialMechanical engineeringEngineeringInternal combustion engine

Abstract

fetched live from OpenAlex

This study investigates the potential factors which may cause cylinder bore distortion in V6 aluminum engine block with cast-in gray iron liners. In this research, the microstructure, mechanical properties and residual stress of 319 type aluminum alloy engine blocks were analyzed from top to bottom along the interbore regions in the TSR, T7 heat treated and service (dyno) tested conditions. The results suggest that the cooling rate increased significantly along the cylinder in the vicinity of the chill plate at the bottom of the engine block. This caused a significant refinement in the microstructure which increased the hardness and tensile strength at the bottom of the cylinder relative to the top. The increased strength at the bottom of the cylinder prevented the rapid relief of residual stress at elevated temperature, which suggests that the bottom of the cylinder is less susceptible to cylinder distortion.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.018
GPT teacher head0.245
Teacher spread0.227 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

Citations3
Published2021
Admission routes1
Has abstractyes

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