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Record W2213287224 · doi:10.4271/2004-01-0732

Microstructure and Mechanical Properties of Welded Thermoplastics

2004· article· en· W2213287224 on OpenAlexaff
Y. Charles Lu, Doug M. Shinozaki, S. Herbert

Bibliographic record

VenueSAE technical papers on CD-ROM/SAE technical paper series · 2004
Typearticle
Languageen
FieldEngineering
TopicMechanical Behavior of Composites
Canadian institutionsWestern University
Fundersnot available
KeywordsMicrostructureWeldingMaterials scienceComposite material

Abstract

fetched live from OpenAlex

<div class="htmlview paragraph">Thermoplastics have been used increasingly for automobile components for both interior and under-the-hood applications. The plastic parts are made through various molding process such as compression molding, injection molding and blow molding. For parts with large or complicated geometry, small portions of the part may have to be molded first, then joined together using a welding process. The welded regions usually exhibit inhomogeneous and inferior mechanical performance compared to the bulk regions due to the differences in thermal history.</div> <div class="htmlview paragraph">The microstructures and mechanical properties of welded thermoplastics have been examined using hot-plate welded polyethylene. The specimens are prepared at various thermal conditions to simulate the real welding process. The thermal properties in welds are monitored using DSC (Differential Scanning Calorimetry) and the crystallinities are calculated. The microstructures in welds are examined using optical microscope and TEM (transmission electron micrograph) and smaller lamellar size is observed. The mechanical properties of the weld are examined using tensile test and special-constructed microindentation test. Both tests show that the mechanical properties in the welds are inferior to that in the bulks but they can be improved by proper heat treatments such as annealing.</div>

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.827
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
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 head0.010
GPT teacher head0.205
Teacher spread0.196 · 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.

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
Published2004
Admission routes1
Has abstractyes

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