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Manufacturing of near-net-shape composite parts using Tailored Fiber Placement preforms

2023· article· en· W4320014359 on OpenAlex
Marie‐Claude Bélanger, Patricia Forcier, Alain Bujold, Simon Pesant, Serge Pagé

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueIOP Conference Series Materials Science and Engineering · 2023
Typearticle
Languageen
FieldEngineering
TopicEpoxy Resin Curing Processes
Canadian institutionsCegep de Saint Hyacinthe
Fundersnot available
KeywordsMaterials scienceTransfer moldingCompactionComposite materialMolding (decorative)Silicone rubberComposite numberSiliconeNear net shapeConsolidation (business)FiberMold

Abstract

fetched live from OpenAlex

Abstract This paper demonstrates the feasibility of manufacturing a light and low cost near-net-shape vertebra from Tailored Fiber Placement (TFP) preforms. The TFP preforms were produced flat and sewn together at the ends by a classic sewing process. Low pressure Vacuum Assisted Resin Transfer Molding (VARTM) was used for the consolidation and impregnation of the preforms. The molding strategy was oriented towards the use of silicone conformers to ensure a compaction that well fits the preform despite its imperfections. The resulting part required a light finishing. The mass was reduced by 40% and the manufacturing cost by 76% compared to the original part.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.108
Threshold uncertainty score0.943

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 communication0.0000.001
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 head0.022
GPT teacher head0.228
Teacher spread0.206 · 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