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Record W2911935450 · doi:10.1115/1.4042593

Strength and Ductility Loss of CPVC Pipe Due to Exposure to Primer

2019· article· en· W2911935450 on OpenAlexafffund
Bingjun Chen, P.‐Y. Ben Jar, Pierre Mertiny, Robert Prybysh

Bibliographic record

VenueJournal of Pressure Vessel Technology · 2019
Typearticle
Languageen
FieldEngineering
TopicMechanical Behavior of Composites
Canadian institutionsNational Institute for NanotechnologyUniversity of Alberta
FundersMitacsUniversity of Alberta
KeywordsMaterials sciencePrimer (cosmetics)Composite materialChlorinated polyvinyl chlorideDuctility (Earth science)Ultimate tensile strengthPolyvinyl chlorideChemistryCreep

Abstract

fetched live from OpenAlex

Abstract This paper investigates the influence of primer on mechanical properties and fracture behavior for ring specimens, prepared from the commercial chlorinated polyvinyl chloride (CPVC) pipe. After immersing the specimens in primer for 30 min, the specimens were dried for eight different periods, ranging from half day to 113 days, and then fractured in tension along the hoop direction. The results suggest that the longer the drying time, the higher the recovered strength. After the longest drying time of 113 days, the primer-affected-zone showed strength recovery up to 63% of the strength for the virgin pipe. However, such a level of recovery cannot be achieved for ductility. The examination of specimens indicated that the exposure to primer created a core–shell structure on the cross section, of which the area ratio was independent of the drying time. It is believed that exposure to primer caused swelling and formed the shell region. The presence of the shell region has two roles in the ductility reduction. One is to provide multiple sites along the border between the shell and the core regions for crack initiation and the other to enhance stress concentration at the crack tip. This paper concludes that exposure to primer in the solvent welding process may reduce ductility of the CPVC pipe, thus affecting its resistance to slow crack growth in long-term applications.

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 score0.036
Threshold uncertainty score0.582

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 communication0.0000.000
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 head0.006
GPT teacher head0.228
Teacher spread0.222 · 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

Citations5
Published2019
Admission routes2
Has abstractyes

Explore more

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