Factors affecting the properties of composites made by 4D printing (moldless composites manufacturing)
Bibliographic record
Abstract
3D printing is the process where layers of materials are deposited to make structures of complex geometries. 4D printing is a process that combines 3D printing with the application of some activating agent in order to change the shape of the manufactured part after the process, e.g. the flat structure will change its shape to take up the desired complicated shape such as “curved” or “S” shaped. As such the 3D printing process does not have to spend time to print the intricate parts, and the process can be faster. The requirement for 4D printing is that materials with special characteristics that are responsive to an activating agent need to be used. 4D printing of composites utilizes the same concept of 4D printing, except that the materials used are long fiber composite materials. 4D printing of composites utilizes the shrinkage of the matrix resin, and the difference in coefficients of thermal contraction of layers with different fiber orientations to activate the change in shape upon curing and cooling. This behavior can be used to make parts with curved geometries without the need for a complex mold. As such manufacturing of pieces of curved shapes can be fast and economical. However, the degree of shape changing depends on the material properties, the fiber orientation, the lay up sequence and the manufacturing process. This paper presents the results obtained from a study on the effects of these aspects on the shape of the curved parts.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".