Enhanced prediction of mechanical properties in interwoven 3D-printed structures by integrating finite element analysis and design of experiments
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Representative volume element (RVE) models have been widely used to study the influence of additive manufacturing parameters on the mechanical properties of 3D-printed components. However, prior work primarily focused on simple infill patterns, often neglecting the complexities of interwoven geometries. This study introduces a methodology that integrates finite element analysis (FEA) with a statistical approach to predict the mechanical properties of novel interwoven structures produced by the z-stitching technique. Enhanced performance characteristics are explored by strategically aligning and stitching filaments in multiple planes. The FEA approach is grounded in meso-mechanical analyses using RVEs to predict effective orthotropic properties, specifically evaluating stress–strain behavior, modulus of elasticity, and strength. Mechanical properties derived from FEA-based homogenization were validated against experimental tensile tests. The combined use of numerical modeling and statistical analysis enables an efficient, iterative design process for complex 3D-printed structures, reducing computational demands and experimental efforts.
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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 | 0.001 | 0.001 |
| 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 it