Développement d'une nouvelle approche de déposition de matière permettant de maximiser la densité et minimiser les déviations dimensionnelles des pièces à vert fabriquées par extrusion de matériau
Bibliographic record
Abstract
Additive manufacturing by material extrusion (MEX) of highly filled polymers shows significant potential for prototyping and rapid manufacturing of complex geometry metallic components. However, a major challenge of this process lies in eliminating the rhomboid voids at the intersection of the four extruded material stands, which present an oblong shape. The size and arrangement of these voids, which limit the achievable green density by this technique, are primarily influenced by the deposition method. To address this challenge, a study on the impact of certain extrusion parameters (layer height, extrusion speed, nozzle temperature, and bed temperature) was conducted to determine the optimal operational range of a new 3D printer using the piston principle to fabricate parts with a stainless-steel powder feedstock (17-4PH). A thorough investigation into the impact of different deposition approaches was then carried out to analyze the influence of printing parameters on the dimensional properties and density of the produced parts. The results showed that by using first bead overlap strategy (with a 20% overlap rate), it was possible to eliminate interlayer rhomboid voids while minimizing dimensional deviations of parts manufactured by MEX. Additionally, it was found that certain strategies, such as the flow multiplier, although popular, could lead to significant dimensional deviations from the nominal value. These deviations are mainly due to material over-extrusion and the significant pressure caused by this strategy in the print head. It was also demonstrated that the first bead overlap strategy, when applied at a 20% level, enabled the production of parts with a maximum dimensional variation limited to 50 µm, without compromising density. This conclusion confirms that this new approach is superior for producing defect-free parts with minimal dimensional variation. These results are of great importance for improving the quality and precision of metallic parts produced by MEX, thus paving the way for new opportunities in various industrial sectors.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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.
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".