Une méthodologie de conception pour la fabrication additive
Bibliographic record
Abstract
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.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".