Tensile Tests of Different Families of Ultra-Lightweight Photosensitive Polymer Microlattices
Bibliographic record
Abstract
Abstract In aerospace applications, achieving lightweight designs is crucial for optimal performance, often necessitating the use of materials with the best stiffness-to-mass ratios when budget permits. At the design level, polymeric microlattice structures can further optimize parts, but their manufacturing remains challenging. Their use in the aerospace industry is still limited due to insufficient knowledge of the mechanical properties associated with machines, materials, and geometrical parameters. This article investigates and cross-compares different photosensitive printing technologies and families of microlattices. We explore how specific microlattice patterns can be utilized to achieve desired structural behaviors beyond the inherent properties of the raw materials. Given the highly intertwined structural effects at micro, meso, and macroscopic levels due to the material addition process in additive manufacturing (AM), our study focuses on UV-based AM technologies for their accessibility and high resolution. Accurate knowledge of mechanical properties is essential for the design process, yet material datasheets often lack standardized information. Therefore, we extend the characterization of UV resin microlattices through extensive experimental testing. We analyze the results of a comprehensive tensile test campaign on various microlattice patterns using digital image correlation to reveal strain distribution within specimens during damage evolution. Our findings provide a more in-depth understanding of multiscale mechanical property propagation across micro, meso, and macroscopic levels in AM of microlattices, thanks to an assessment of the mechanical properties of each resin used and the characterization of the anisotropy of each 3D printer.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".