Static Behavior of 3D Printed Sandwich Beam with Spherical Core for Aircraft Structure
Bibliographic record
Abstract
The mechanical response of sandwich structures is strongly governed by the geometry of their core; however, spherical core configurations have received limited attention due to fabrication constraints associated with conventional manufacturing techniques.In this study, additive manufacturing (AM) is utilized to introduce and investigate a novel spherical-core sandwich beam designed for lightweight structural applications.The static bending behavior of cantilever sandwich beams subjected to transverse loading is examined through a combined numerical and experimental approach.Finite element simulations were performed using ANSYS Workbench 2023, covering 72 parametric cases to assess the influence of sphere radius, center-to-center spacing distance, and face sheet thickness on static deflection, von Mises stress, and strain energy.Experimental validation was conducted by fabricating and testing eight specimens using 3D printing technology.The numerical predictions show good agreement with the experimental deflection results, with deviations within an acceptable range.The results reveal that increasing the sphere radius and face sheet thickness enhances the bending stiffness and reduces deformation, stress, and strain energy, while increasing the spacing distance leads to a noticeable deterioration in structural performance.These findings confirm the potential of spherical-core sandwich beams as an effective alternative core configuration for achieving high stiffness-to-weight efficiency under static bending loads.
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) | 0.000 | 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 | 0.000 | 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".