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Record W2103469034 · doi:10.1002/pen.10063

The effect of sample preparation on the mechanical properties of nylon 66

2003· article· en· W2103469034 on OpenAlexafffund
P. J. Bates, C. Y. Wang

Bibliographic record

VenuePolymer Engineering and Science · 2003
Typearticle
Languageen
FieldEngineering
TopicMechanical Behavior of Composites
Canadian institutionsRoyal Military College of Canada
FundersAUTO21 Network of Centres of Excellence
KeywordsMaterials scienceComposite materialFlexural strengthPolyamideScanning electron microscopeIzod impact strength testEnhanced Data Rates for GSM EvolutionSurface roughnessGlass fiberPolishingFiberNylon 6Ultimate tensile strengthPolymer

Abstract

fetched live from OpenAlex

Abstract Cutting test specimens from molded plaques is commonly used in mechanical testing. The mechanical properties of these cut specimens may be affected by cutting process as it could introduce extrinsic flaws and thermal effects on cut edge surfaces. The objective of this experimental research is to determine how band saw cutting affects the flexural and impact strengths of 33% short glass fiber (GF) reinforced polyamide 66 (PA66) and unreinforced PA66. The specimens for the flexural and impact tests were obtained by cutting molded plaques using different blade types, blade speeds, feed rates, and levels of polishing. Results were compared with those from uncut specimens. Surface morphologies of the specimens' cut edges (photographs and roughness) were assessed using Scanning Electron Microscopy and PRK Perthometer, respectively. The results indicated that higher flexural and impact strengths of cut specimens of 33% GF reinforced PA66 were achieved with high blade speed, low work piece feed rate and using a high number of teeth per unit length. For unreinforced PA66, higher impact strengths were achieved at low blade speeds and work piece feed rates.

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)0.001
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 score0.057
Threshold uncertainty score0.161

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0000.000

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 head0.011
GPT teacher head0.214
Teacher spread0.203 · 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

Citations7
Published2003
Admission routes2
Has abstractyes

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