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Record W2128742840 · doi:10.3139/217.2712

Characterization of Thermoplastic Laser-welded Joints

2012· article· en· W2128742840 on OpenAlexaff
M. H. Al-Wohoush, Musa R. Kamal

Bibliographic record

VenueInternational Polymer Processing · 2012
Typearticle
Languageen
FieldEngineering
TopicMechanical Behavior of Composites
Canadian institutionsMcGill University
Fundersnot available
KeywordsMaterials scienceComposite materialPolyamideWeldingThermoplasticUltimate tensile strengthPolycarbonateScanning electron microscopeLaserLaser power scalingOptical microscopeGlass fiberLaser beam weldingOptics

Abstract

fetched live from OpenAlex

Abstract Laser-welded joints were prepared using moldings of polycarbonate, polyamide-6, and polyamide-6 reinforced with 30% glass fibers. Preparation of thin polished samples of the joints facilitated examination of the laser-affected zone (LAZ). The samples were examined using optical and polarized light microscopy, as well as scanning electron microscopy. The study evaluated the influence of laser power on the geometry and dimensions of LAZ, in both the absorbent and non-absorbent parts of the joints. The results indicated a correlation between LAZ dimensions and the tensile strength of the laser-welded joints. There is evidence of melt and fiber migration in the LAZ.

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 score0.016
Threshold uncertainty score0.353

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.014
GPT teacher head0.241
Teacher spread0.228 · 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

Citations4
Published2012
Admission routes1
Has abstractyes

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