A Novel Dual Curved Cubic (DCC) structure with improved compressive strength
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Bibliographic record
Abstract
Lattice structures are valuable in engineering applications due to their high strength-to-weight ratios and excellent energy absorption capabilities. Their use has expanded considerably with advancements in additive manufacturing, enabling the production of complex designs. This study investigates a novel lattice structure inspired by “inosculation” phenomenon, a natural grafting process in plants and trees. The resulting lattice structure, termed Dual Curved Cubic (DCC), was represented via a Matlab-based implicit modeling algorithm for defining curved strut lattices. Test specimens of the DCC structure at 25% relative density and 70° curvature angle were fabricated with photo-sensitive resin in a vat photopolymerization apparatus and subjected to quasi-static compressive tests. The numerical and experimental results for the DCC were then compared with conventional structures, Body-Centered Cubic (BCC) and Octet lattices, to evaluate the relative yield strength, elastic modulus, and energy absorption properties. The DCC demonstrated a superior compressive strength of 4.53 MPa, exceeding that of the Octet (3.17 MPa) and BCC (2.28 MPa) lattice structures at the same relative density. The energy absorption of the DCC (42.61%) was observed to be lower than those of both Octet (68.86%) and BCC (64%) at the investigated curvature angle and relative density. However, by reducing the curvature and density, a significant improvement in energy absorption was observed, making these geometric parameters useful controls for optimizing mechanical performance. • A novel Dual Curved Cubic (DCC) cell inspired by the inosculation in trees was developed. • Potentially the first study on modelling curved strut lattices using signed distance functions. • A 70°DCC structure showed a higher compressive strength than BCC and Octet cells. • A Gibson–Ashby model predicts yield strength and elastic modulus vs relative density.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it