Bending behavior of hybrid sandwich composite structures containing 3D printed PLA lattice cores and magnesium alloy face sheets
Bibliographic record
Abstract
In this study, the bending behavior of novel magnesium alloy facesheet and 3D-printed polylactic acid (PLA) lattice core sandwich panels with enhanced interface adhesion has been investigated by joint experiment and simulation method. A reliable numerical model for predicting three-point bending behavior of hybrid sandwich panels was developed and an explicit finite element analysis using ABAQUS/Explicit with a self-written VUMAT user subroutine was conducted. Meanwhile, hybrid sandwich panels were evaluated experimentally by three-point bending tests in order to validate the results obtained in the FEA. The comparison results have shown that the FEA predictions are consistent with the experimental tests. The bending resistance of the hybrid PLA core/Mg alloy composite sandwich panel is better than that of the integrated PLA sandwich panel. Among the four lattice geometries studied, namely body-centered cubic (BCC), BCC with gradient distribution of struts (BCCG), BCC with vertical struts connecting all nodes (BCCV), and face and body centered cubic unit cell with vertical struts (F2BCCZ), the sandwich structure with BCCV lattice cores has the better bending bearing capacity. The core configuration and core density have a coupling effect on the bending properties and failure modes of the hybrid sandwich panels.
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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 | 0.000 | 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".