MétaCan
Menu
Back to cohort
Record W3196035319 · doi:10.11159/icmie21.109

Design, Manufacturing and Testing of Pipe Fittings

2021· article· en· W3196035319 on OpenAlexvenueno aff
M. Evren Toygar, Selin Kirdiş, Mustafa Yeğin, Elvan Pehlivan, Umut Doğan, Özkan Deniz, Mustafa Turhan Çoban

Bibliographic record

VenueProceedings of the World Congress on Mechanical, Chemical, and Material Engineering · 2021
Typearticle
Languageen
FieldEngineering
TopicMechanical stress and fatigue analysis
Canadian institutionsnot available
Fundersnot available
KeywordsComputer scienceReliability engineeringEngineering

Abstract

fetched live from OpenAlex

Fasteners have a very significant place in machine design, and they are the indispensable elements of many sectors such as automotive, electrical electronics, metal industry.Pipe fittings are one of the basic types of fasteners, and the most significant feature of pipe fittings is that they provide impermeability.In the first part of the study, pipe fittings were designed and manufactured.In the second part of the study, experiments were conducted to analyze the effect of the surface roughness, fluid pressure and material type on the pipe fitting's sealing performance with the help of the measured torques that prevent leakage.In the following sections, this torque is referred to as sealing torque.Experimental results are consistent with mathematically calculated results and SolidWorks Simulation results.Additionally, it is thought that the result of the performed experiments in this study can help the pipe fitting manufacturers in terms of material and surface roughness selection.

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)0.002
metaresearch head score (Gemma)0.004
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 score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.012
GPT teacher head0.189
Teacher spread0.177 · 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

Citations2
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueProceedings of the World Congress on Mechanical, Chemical, and Material EngineeringSame topicMechanical stress and fatigue analysisFrench-language works237,207