A Novel Dual Curved Cubic (DCC) structure with improved compressive strength
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.
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) | 0.002 | 0.001 |
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".