Mechanical properties of a new 3D printed gradable porous cellular lattice structure considering surface-to-volume capability
Bibliographic record
Abstract
In recent years, significant progress in additive manufacturing (AM) techniques has enabled the design and manufacturing of cellular structures with precise micro-architecture and complex cell topologies. This advancement has significantly bolstered the manufacturing of porous materials, leading to the enhancement of their mechanical properties without compromising their lightweight nature. In this paper, a new cellular structure with cell migration capability for the regeneration of bones is designed and introduced. Uniform and functional graded porous lattice structures are manufactured based on the new unit cell with AM technology. The mechanical properties of the unit cell and the cellular structures in two directions are derived analytically. To validate analytical mathematical equations, numerical modeling and experimental tests are conducted. The results of experimental, numerical, and analytical studies are compared to each other and show good agreement. The discrepancy between the yield stress values obtained from the analytical and experimental models of the functional specimen falls within an acceptable range of error regarding usual uncertainties in manufacturing process and surface quality besides the theoretical limitations. Finally, we utilize the genetic algorithm to perform single and multi-objective optimizations on the performance of the unit cell. The optimization results indicate that the new unit cell with optimized lengths sides can improve cell migration and bone regeneration according to the mentioned criteria for the optimization.
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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) | 0.000 | 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 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".