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Record W2943663204 · doi:10.1115/1.4043680

Design of Conformal Porous Structures for the Cooling System of an Injection Mold Fabricated by Additive Manufacturing Process

2019· article· en· W2943663204 on OpenAlexafffund
Yunlong Tang, Zhengyang Gao, Yaoyao Fiona Zhao

Bibliographic record

VenueJournal of Mechanical Design · 2019
Typearticle
Languageen
FieldEngineering
TopicInjection Molding Process and Properties
Canadian institutionsMcGill University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMoldConformal mapMolding (decorative)Materials sciencePorosityPressure dropWater coolingMechanical engineeringComposite materialMechanicsEngineeringGeometry

Abstract

fetched live from OpenAlex

Abstract The cooling system of plastic injection mold plays a critical role during the injection molding process. It not only affects part quality but also its cycle time. Traditionally, due to the limitations of conventional drilling methods, the cooling system of the injection mold usually consists of simple paralleled straight channels. It seriously limits the mobility of cooling fluid, which leads to the low cooling efficiency for the parts with complex free-form surfaces. In this research, an innovative design method for the cooling system of an injection mold is proposed by using conformal porous structures. The size and shape of each cell in the conformal porous structure are varied according to the shape of an injection molded part. Design cases are provided at the end of this paper to further illustrate the efficiency of the proposed method. Compared with those existing design methods for the uniform porous structures, the proposed method can further reduce the nonuniformity of the mold surface temperature distribution and decrease the pressure drop of the cooling system.

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.019
GPT teacher head0.229
Teacher spread0.210 · 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 designSimulation or modeling
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

Citations21
Published2019
Admission routes2
Has abstractyes

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Same venueJournal of Mechanical DesignSame topicInjection Molding Process and PropertiesFrench-language works237,207