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Record W4391389589 · doi:10.1007/s00170-024-13071-3

Residual stress measurement in engine block—an overview

2024· article· en· W4391389589 on OpenAlexaff
Houman Alipooramirabad, Sina Kianfar, Anna Paradowska, Reza Ghomashchi

Bibliographic record

VenueThe International Journal of Advanced Manufacturing Technology · 2024
Typearticle
Languageen
FieldEngineering
TopicWelding Techniques and Residual Stresses
Canadian institutionsOkanagan University CollegeUniversity of British Columbia, Okanagan CampusUniversity of British Columbia
Fundersnot available
KeywordsResidual stressBlock (permutation group theory)ResidualStress (linguistics)Cylinder blockEngineeringMechanical engineeringComputer scienceMaterials scienceMathematicsComposite materialAlgorithmGeometry

Abstract

fetched live from OpenAlex

Abstract Significant residual stresses are often generated during the manufacturing of cast Al-Si alloy engine blocks due to differential cooling rates, the mismatch in the thermo-physical properties of adjacent materials in direct contact and volumetric changes caused by solid-state phase transformations during cooling. These may be modified during heat treatment and operation. These residual stresses may lead to distortion (affecting performance and economy) or premature failure of the engine block. For this reason, it is of fundamental importance to have reliable numerical and experimental methods for characterizing the residual stresses in the engine blocks at several steps during the manufacturing process. Sectioning and neutron diffraction techniques have been widely used to determine the residual stresses in the engine blocks. Numerical techniques have been developed to predict these residual stress but require experimental validation. The authors reviewed several numerical and experimental studies of residual stress evolution in engine blocks and showed how the residual stresses, microstructures, and mechanical properties are correlated.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.905
Threshold uncertainty score0.392

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.024
GPT teacher head0.282
Teacher spread0.259 · 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

Citations12
Published2024
Admission routes1
Has abstractyes

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