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Record W2309689014

Laminated Injection Mould with Conformal Cooling Channels: Optimization, Fabrication and Testing

2013· article· en· W2309689014 on OpenAlexaff
Hossein Ahari, Amir Khajepour, Sanjeev Bedi

Bibliographic record

VenueUWSpace (University of Waterloo) · 2013
Typearticle
Languageen
FieldEngineering
TopicInjection Molding Process and Properties
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsConformal mapStackingMaterials scienceFabricationDie (integrated circuit)Mechanical engineeringSheet metalWater coolingComposite materialEngineering drawingStructural engineeringEngineeringGeometryNanotechnologyMathematics
DOInot available

Abstract

fetched live from OpenAlex

Conformal cooling channels follow the cavity shape and can provide a better cooling performance in injection moulds. Laminated tooling is one of the techniques for manufacturing injection moulds with conformal cooling systems. A laminated tool is made by stacking metal sheets of varying thicknesses from which pre–calculated profiles have been cut. The stacked sheets result in a jagged die surface that has to be finished before use. Although larger number of small thickness sheets result in small irregularities that can be finished easily, it increases the cost of profile cutting process. Therefore, one of the issues in laminated tooling is determination of sheet thicknesses so that the laminated die can be made optimally. In this paper, an optimization method is presented to find the best size of the various laminas based on CAD model surface geometry such that the surface jaggedness and the number of slice is reduced at the same time. The final mould is fabricated based on suggested optimization method. It is then tested to show the improvement in cooling performance as compared to the same die with conventional cooling channels.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
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)0.0010.000

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 head0.156
Teacher spread0.145 · 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

Citations5
Published2013
Admission routes1
Has abstractyes

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Same venueUWSpace (University of Waterloo)Same topicInjection Molding Process and PropertiesFrench-language works237,207