MétaCan
Menu
Back to cohort
Record W1542978917 · doi:10.4271/2010-01-0965

Bolt Load Relaxation and Fatigue Prediction in Threads with Consideration of Creep Behavior for Die Cast Aluminum

2010· article· en· W1542978917 on OpenAlexaff
Michael A. DeJack, Yue Ma, Russell Craig

Bibliographic record

VenueSAE technical papers on CD-ROM/SAE technical paper series · 2010
Typearticle
Languageen
FieldEngineering
TopicMetallurgy and Material Forming
Canadian institutionsChrysler (Canada)
Fundersnot available
KeywordsCreepAluminiumMaterials scienceRelaxation (psychology)Die (integrated circuit)Stress relaxationStructural engineeringComposite materialComputer scienceEngineeringPsychology

Abstract

fetched live from OpenAlex

<div class="section abstract"><div class="htmlview paragraph">Bolt load retention is an important consideration in developing highly loaded structures with die cast aluminum due to its creep behavior. During development of a new cylinder block design, an objective was established to optimize the main bearing bolted joint design for bolt load retention. A creep model was developed from literature data, applied using detailed thread sub-models, and calibrated to produce results in good agreement with observed bolt load loss. This creep model was applied in sensitivity studies to investigate the effect of variation in thread engagement and installation load on bolt load loss. Selected thread sub-models were used with and without creep considerations to estimate high cycle fatigue safety in the bulkhead thread roots of a highly loaded cylinder block. Results from the investigation demonstrate that bolt clamp load loss due to creep can be simulated, and sensitivity studies can provide practical design guidance. Further, including the effects of creep can provide estimates for stress relaxation in threads, and its influence in fatigue predictions is assessed.</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 categoriesMeta-epidemiology (narrow)
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 score0.987
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0010.001
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 head0.014
GPT teacher head0.239
Teacher spread0.224 · 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.

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

Citations1
Published2010
Admission routes1
Has abstractyes

Explore more

Same venueSAE technical papers on CD-ROM/SAE technical paper seriesSame topicMetallurgy and Material FormingFrench-language works237,207