Compressive and bending properties of 3D-printed wood/PLA composites with Re-entrant honeycomb core
Bibliographic record
Abstract
This study investigates the mechanical behavior of sandwich structures comprising a re-entrant honeycomb core structure created using wood/polylactic acid (PLA) filaments via fused deposition modeling (FDM) technology. The sandwich structures were manufactured with different face layer thicknesses (0 mm, 0.8 mm, and 1.6 mm) and various core topologies. Material characterizations included bending tests, compressive tests, and finite element analysis (FEA) to identify stress concentration areas. In-plane and out-of-plane re-entrant honeycomb core structure specimens were tested to examine the bending properties. In addition, flatwise and edgewise compressive tests were performed to investigate the structures' compressive properties. Furthermore, the modulus of elasticity for each specimen was determined using the finite element technique (FEM). The results reveal that increasing the thickness of the face layer significantly enhances the structure's resistance to bending forces. Furthermore, specimens with an in-plane orientation demonstrated better bending strength compared to those with an out-of-plane orientation due to increased material underloading. In flatwise compressive tests, specimens without a face layer exhibited the highest strength, attributed to their greater displacement. In contrast, edgewise compressive tests showed significant buckling behavior of the face sheet, the maximum stress increased proportionally with the thickness of the face layer, reaching its peak at a skin thickness of 1.6 mm. The findings are validated by ANSYS analysis, which closely mirrors the experimental results, providing insight into flexural modulus, modulus of elasticity, and stress concentration. These findings indicate that architected core structures could be efficiently utilized to improve bending/compressive characteristics and failure mechanisms, providing valuable insights into the mechanical response of sandwich structures for different industrial applications. • Thicker face layer enhances 3D printed wood/PLA composite's resistance to bending forces. • In-plane core orientation outperforms out-of-plane in bending strength, crucial for design. • Addition of face layer significantly increases compressive strength in flatwise and edgewise orientations. • Material exhibits anisotropic properties with varying failure modes under different stresses. • Experimental tests and Ansys simulations show good convergence, validating study's accuracy.
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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".