Enhanced prediction of mechanical properties in interwoven 3D-printed structures by integrating finite element analysis and design of experiments
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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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".