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Record W2032399920 · doi:10.1179/174328907x177617

Real time diagnostics of gas/water assisted injection moulding using integrated ultrasonic sensors

2007· article· en· W2032399920 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenuePlastics Rubber and Composites Macromolecular Engineering · 2007
Typearticle
Languageen
FieldEngineering
TopicInjection Molding Process and Properties
Canadian institutionsNational Research Council CanadaCarleton UniversityMcGill University
FundersEngineering and Physical Sciences Research Council
KeywordsMaterials scienceUltrasonic sensorInjection mouldingUltrasoundPolymerComposite materialInstrumentation (computer programming)BubbleUltrasound energyWorking fluidAcousticsMechanical engineeringMechanics

Abstract

fetched live from OpenAlex

An ultrasound sensor system has been applied to the mould of both the water and gas assisted injection moulding processes. The mould has a cavity wall mounted pressure sensor and instrumentation to monitor the injection moulding machine. Two ultrasound sensors are used to monitor the arrival of the fluid (gas or water) bubble tip through the detection of reflected ultrasound energy from the fluid polymer boundary and the fluid bubble tip velocity through the polymer melt is estimated. The polymer contact with the cavity wall is observed through the reflected ultrasound energy from that boundary. A theoretically based estimation of the residual wall thickness is made using the ultrasound reflection from the fluid (gas or water) polymer boundary while the samples are still inside the mould and a good correlation with a physical measurement is observed.

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.

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 score0.122
Threshold uncertainty score0.971

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 head0.006
GPT teacher head0.191
Teacher spread0.185 · 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