Non‐Planar Multiprocess Additive Manufacturing of Multifunctional Composites
Bibliographic record
Abstract
Abstract Multiprocess additive manufacturing (AM) consists of integrating different 3D printing techniques to enable the fabrication of multifunctional parts, based on their geometry and material properties. The combination of fused filament fabrication (FFF) and direct ink writing (DIW) techniques, respectively involving thermoplastics and thermosetting polymers (or composites), often focuses on planar and small‐scale applications (i.e., few cm), with limited nozzle orientation freedom for the fabrication of complex parts. Many industries, such as the aerospace sector, could benefit from the AM of lightweight multifunctional parts. For instance, one of the key aircraft components, the abradable seal coating, is applied on gas turbine engines casing to increase engine efficiency and is mechanically abraded by the rotor blades during engine startup. Abradable coatings made of thermosetting polymer could be 3D‐printed using a multiprocess to obtain more functionalities. In this work, a non‐planar multiprocess AM approach involving FFF of a complex large sandwich panel structure with low material density and large‐scale DIW of an abradable thermosetting coating with controlled porosity for sound absorption potential, and better mechanical abradability than a commercial product, is presented. This multiprocess AM approach can be used to manufacture lightweight multifunctional structural parts for the automotive or aerospace industries.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".