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Record W2094421856 · doi:10.1115/pvp2009-77810

Factors Affecting High Temperature Relaxation Behaviors of Expanded PTFE Gaskets

2009· article· en· W2094421856 on OpenAlexaff
Walter Lee, Abdel‐Hakim Bouzid, James Huang

Bibliographic record

VenueVolume 2: Computer Applications/Technology and Bolted Joints · 2009
Typearticle
Languageen
FieldEngineering
TopicTribology and Wear Analysis
Canadian institutionsÉcole de Technologie Supérieure
Fundersnot available
KeywordsGasketStress relaxationRelaxation (psychology)Materials scienceStress (linguistics)LogarithmLogarithmic scaleComposite materialCreepPhysicsMathematics

Abstract

fetched live from OpenAlex

High temperature behaviors of expanded PTFE gaskets have been studied using the hot blow-out test (HOBT) method. The results suggest that the operating stress and temperature measurements of a tested gasket form a linear relationship on a logarithmic plot, and that higher assembly stress causes proportionally larger degrees of stress relaxation. In contrast, the cooling curve follows a linear relationship in a semi-logarithmic plot (only stress converted). The new formulation and methodology so derived has permitted a better understanding of external and internal factors on relaxation behaviors of expanded PTFE-based gaskets. For example, for a gasket design incorporating a corrugated metal insert, the relaxation curve stays relatively flat until the temperature reaches about 90–100°F, where the stress reduction starts to follow a linear trend parallel to, but above the relaxation curve of the expanded PTFE gasket without the insert. Essentially, the metal insert “delays” the effect of temperature on relaxation, and produces an “effective assembly stress” that is about 13% higher than the actual assembly stress. The use of Belleville washers has shown a similar phenomenon, but with a longer delay and higher effective assembly stress. Finally, the effect of in-process retightening, or hot retorquing, is quantitatively assessed. The contrasting relaxation behaviors of the material by different retightening methods (hot retorque versus post-process retorque in a cooled state) will be discussed.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

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 head0.005
GPT teacher head0.204
Teacher spread0.199 · 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 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
Published2009
Admission routes1
Has abstractyes

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