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Record W3046398132 · doi:10.11159/htff20.181

An Approach to Modelling the Manufacturing Process of Thermoset Composite Reinforced with 3D Woven

2020· article· en· W3046398132 on OpenAlexvenueno aff
Mikhail Kiauka, Pudeleva Olga, V. V. Sergeev, Aleksandr Tamm, Alexey Borovkov

Bibliographic record

VenueProceedings of the World Congress on Mechanical, Chemical, and Material Engineering · 2020
Typearticle
Languageen
FieldEngineering
TopicAdditive Manufacturing and 3D Printing Technologies
Canadian institutionsnot available
Fundersnot available
KeywordsThermosetting polymerComposite numberMaterials scienceProcess (computing)Composite materialManufacturing process3D printingComputer scienceManufacturing engineeringEngineeringProgramming language

Abstract

fetched live from OpenAlex

This paper proposes the general approach to modelling the manufacturing process of thermoset composite reinforced with 3D woven based on the digital twin concept.The focus is on the methodology for determining the permeability of a dry 3D woven.Simulation results in ANSYS CFX are presented.Also, a curing simulation technique in ABAQUS with user subroutines for a thermosetting composite is described.The results of curing simulation for the Kamal-Sourour model are presented in more detail and the prediction of shape distortion for a composite U-shaped part as a result of chemical shrinkage and thermal expansion is performed.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.190
Teacher spread0.181 · 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

Citations1
Published2020
Admission routes1
Has abstractyes

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