Mechanical Performance and Failure Analysis of a 3D-Printed “Continuous Layer–Lattice Layer–Continuous Layer” Sandwich Structure
Bibliographic record
Abstract
Sandwich structures are engineered with continuous layers surrounding the inner lattices, which combines the advantages of the high strength of the continuous layer and the light weight of the lattice layer. They are widely employed in weight-critical energy-absorbing engineering fields such as aerospace, automobile, and robotics. However, the application of sandwich structures made of polymer matrix composites is still limited due to lack of essential performance investigation and adequate reference data. The following innovative works are accomplished in this paper: (i) Continuous long glass fiber (CGF) is employed within the continuous layer of the sandwich structure, with composite short carbon fiber/polyamide (SCF/N) applied within the lattice layer. (ii) Sandwich structures with different cell types and orientations of the lattice infills are designed and prepared by additive manufacturing. (iii) The basic mechanical properties of the sandwich structures, i.e., the bi-directional tension/compression compound performance, failure modes and mechanisms in characteristic directions, are analyzed systematically. (iv) The effects of geometric features on the three-point bending properties of L-shaped sandwich structures are investigated and compared with those of pure SCF/N structures. The results show that the bending resistance per unit weight was up to 54.3% larger than that of pure SCF/N, while the weight could be decreased by 49%, and the bending flexibility before fracture could be increased by 44%. These studies contribute fundamental research data to the application of sandwich structures prepared by fiber reinforced polymer matrix composites.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".