Impact Performance of 3D Printed Sandwich Structures: The Role of Core Geometry in Energy Absorption
Bibliographic record
Abstract
Innovative sandwich structures have gained prominence due to their potential to revolutionize industries with multifunctional performance. This study investigates the impact of different core topologies on the energy absorption capability of 3D-printed sandwich panels. Triply periodic minimal surfaces-based lattice structures and bioinspired spherical closed-cell foam structures were designed and compared against a traditional honeycomb structure to find the most suitable core topology. The fused deposition modeling technique was used to print samples in polylactic acid. The mechanical behavior of the 3D printing material was comprehensively characterized through a uniaxial tensile test. The sandwich panels were subjected to low-velocity impact loads to determine the dependence of their mechanical properties’ responses on their topological features. Deformation mechanisms were investigated experimentally and numerically using ANSYS. The impact of cellular core topologies on deformation mechanisms, multi-hit (and impact location), and energy absorption capabilities demonstrated the possibility of enhancing mechanical performance of the panels. It is found that the sandwich panels with Tetra Radial and Schwarz P core topologies exhibit higher performance, denoted by higher dynamic energy absorption (up to 11% and 16%, respectively, for the 1st impact) and stiffness (up to 42%, and 43%, respectively, for the 1st impact) than the honeycomb structure (with a constant relative density). Significant enhancements in energy absorption, particularly in Schwarz P and Mono Radial panels compared to the previously reported Octet core structure, offer valuable insights for lightweight, durable 3D-printed sandwich structures, with broad applications in multifunctional industries and future trends in materials engineering, promising applications in aerospace, automotive, and construction for the development of weight-efficient, structurally robust materials.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".