MétaCan
Menu
Back to cohort
Record W4399286670 · doi:10.3390/mi15060734

Electroforming of Personalized Multi-Level and Free-Form Metal Parts Utilizing Fused Deposition Modeling-Manufactured Molds

2024· article· en· W4399286670 on OpenAlexafffund
Hazem Hamed, Sayed MohammadAli Aghili, Rolf Wüthrich, Jana D. Abou-Ziki

Bibliographic record

VenueMicromachines · 2024
Typearticle
Languageen
FieldEngineering
TopicAdditive Manufacturing and 3D Printing Technologies
Canadian institutionsConcordia UniversityOntario Tech University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsElectroformingFabricationMoldFused deposition modelingMaterials scienceDeposition (geology)Rapid prototypingProcess (computing)3D printingNanotechnologyProcess engineeringEngineeringComputer scienceMetallurgyComposite materialLayer (electronics)

Abstract

fetched live from OpenAlex

Adapting to the growing demand for personalized, small-batch manufacturing, this study explores the development of additively manufactured molds for electroforming personalized metal parts. The approach integrates novel multi-level mold design and fabrication techniques, along with the experimental procedures for the electroforming process. This work outlines design considerations and guidelines for effective electroforming in additively manufactured molds, successfully demonstrating the production of composite metal components with multi-level and free-form geometries. By emphasizing cost efficiency and part quality, particularly for limited-thickness metal components, the developed technique offers distinct advantages over existing metal additive manufacturing methods. This approach establishes itself as a flexible and durable method for metal additive manufacturing, expanding the scope of electroforming beyond traditional constraints such as thin-walled hollow structures, 2D components, and nanoscale applications.

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 score0.429
Threshold uncertainty score0.873

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 head0.027
GPT teacher head0.245
Teacher spread0.218 · 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

Citations2
Published2024
Admission routes2
Has abstractyes

Explore more

Same venueMicromachinesSame topicAdditive Manufacturing and 3D Printing TechnologiesFrench-language works237,207