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Record W1512007826

Une méthodologie de conception pour la fabrication additive

2011· preprint· fr· W1512007826 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.

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

Bibliographic record

VenuePolyPublie (École Polytechnique de Montréal) · 2011
Typepreprint
Languagefr
FieldEngineering
TopicManufacturing Process and Optimization
Canadian institutionsPolytechnique Montréal
Fundersnot available
KeywordsFabricationComputer scienceManufacturing engineeringEngineering drawingEngineering
DOInot available

Abstract

fetched live from OpenAlex

Les méthodologies de Design for Assembly et de Design for Manufacturing visent à rendre les produits plus faciles à fabriquer et à assembler en se basant sur les caractéristiques des procédés actuels de fabrication, toutefois ces caractéristiques ne s'appliquent plus lorsqu'on prend en compte les nouvelles capacités de la Fabrication Additive. Cet article décrit une méthodologie de conception pour la Fabrication Additive qui guide l'utilisateur vers l'optimisation d'un produit en utilisant les capacités de ces nouveaux procédés de fabrication. La méthodologie proposée est ensuite appliquée à un assemblage mécanique. Abstract - Design for Assembly and Design for Manufacturing methodologies aim to make products easier to manufacture and assemble by basing itself off the characteristics of actual manufacturing processes, however these characteristics aren't applicable when taking into account the new capabilities of Additive Manufacturing. This article describes a design methodology for Additive Manufacturing which guides the users towards the optimization of a product using the capabilities of these fabrication processes. The proposed methodology is then applied to a mechanical assembly.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.469
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0020.002
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 head0.248
Teacher spread0.221 · 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