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Record W4402464100 · doi:10.11159/eee24.103

Printed Circuit Boards Manufacturing using Electrodeposition Process:An Innovative Numerical Model

2024· article· en· W4402464100 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.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueProceedings of the World Congress on Electrical Engineering and Computer Systems and Science · 2024
Typearticle
Languageen
FieldEngineering
TopicMaterial Properties and Processing
Canadian institutionsnot available
Fundersnot available
KeywordsPrinted circuit boardProcess (computing)Manufacturing engineeringManufacturing processComputer scienceMaterials scienceEngineeringElectrical engineeringComposite material

Abstract

fetched live from OpenAlex

Electroforming stands as a vital and cost-effective method widely employed not only to mitigate corrosion but also to improve the aesthetic appeal of various products.Through the process of Electrodeposition, a fine layer of the desired metal is meticulously deposited onto a base object, creating a unified and durable coat that not only supports against corrosion but also imparts an exquisite finish, elevating the overall quality and visual allure of the item.In this paper, we propose an innovative modeling approach to simulate Printed Circuit Boards (PCB) manufacturing, based on metal Electrodeposition using the commercial software COMSOL Multiphysics: Electrodeposition Module.In order to ensure the robustness of our numerical model, we undertake a systematic exploration by investigating the impact of various process parameters on the weight and thickness of the electrodeposited metal.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.258
Threshold uncertainty score0.552

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
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 head0.011
GPT teacher head0.218
Teacher spread0.207 · 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