Triaxial Analysis of Mechanical Properties in Additively Manufactured Layered Material
Bibliographic record
Abstract
ABSTRACT Stereolithography (SLA) 3D printing offers unprecedented opportunities for creating tailored materials with complex geometries. This study quantifies the impact of layer thickness and orientation on the mechanical and acoustic properties of SLA‐printed materials under triaxial stress conditions, a critical yet understudied area. We fabricated models with layer dip angles of 0°, 45°, and 90°, and layer thicknesses of 25, 100, and 160 μm. These samples underwent triaxial compression testing and ultrasonic elastic wave velocity measurements using an Autolab 1500 triaxial load frame. Our findings reveal significant anisotropy in the mechanical properties of the 3D‐printed samples, with up to a 30% increase in uniaxial compressive strength for 45° oriented samples. Additionally, we uncover a novel relationship between layer parameters and acoustic properties, enabling nondestructive quality assessment of 3D‐printed components. This research provides a comprehensive framework for enhancing the performance of 3D‐printed materials in high‐stress applications, with critical insights into the behavior of SLA‐printed materials under complex stress states, useful for aerospace, geomechanics, and biomedical engineering applications.
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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) | 0.001 | 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".