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Record W3213680301 · doi:10.1016/j.ifacol.2021.08.125

Improving Geometric Tolerances in 3D Printable Pneumatic Valves Designed for Respiratory Mechanical Ventilators Amid Covid-19 Pandemic

2021· article· en· W3213680301 on OpenAlexaff
Hossein Gohari, Marcos de Sales Guerra Tsuzuki, Ahmad Barari

Bibliographic record

VenueIFAC-PapersOnLine · 2021
Typearticle
Languageen
FieldEngineering
TopicAdditive Manufacturing and 3D Printing Technologies
Canadian institutionsUniversity of Ontario Institute of Technology
Fundersnot available
KeywordsCoronavirus disease 2019 (COVID-19)Computer sciencePandemicInscribed figureMechanical engineeringEngineeringMathematicsGeometryMedicine

Abstract

fetched live from OpenAlex

Additive manufacturing is the main approach to fabricate freeform and complex shapes specifically when there is a time limit for the production of a part. Manufacturing complex shapes and highly customizable products in a short time is crucial especially during catastrophic events such as earthquakes, hurricanes and even pandemics in which having a mechanical part is vital to save human lives. An internal or external circular geometry is among the main manufacturing shapes that can be seen in many industrial parts which are mainly used as a joint between the components. In this paper, a methodology is presented to minimize the difference between the minimum circumscribed and the maximum inscribed circles constructing a circular feature of a pneumatic valve. The main purpose of this research is to improve the feasibility of using Additive Manufacturing methods in terms of dimensional accuracy to manufacture parts promptly and without the need for a post-processing operation.

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)0.002
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 score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.000
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 head0.044
GPT teacher head0.283
Teacher spread0.238 · 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

Citations1
Published2021
Admission routes1
Has abstractyes

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