Comparisons of the Performance of Novel Lightweight Three-Dimensional Hybrid Composites against GLARE Fiber–Metal Laminate
Bibliographic record
Abstract
The objective of the work presented in this paper is to overcome several major shortcomings of the recently introduced 3D composites (3DCs) and their fiber–metal-laminate renditions (3DFMLs). A new class of lightweight, stiff, and resilient three-dimensional hybrid composites (3DHCs) is introduced in this study, referred to as “inserts enhanced 3D hybrid composites” (IE3DHCs). The performances of all configurations were characterised by conducting three-point flexural tests using a span-to-thickness ratio of 32:1. The flexural performance of 3DFMLs with different core thicknesses was first compared using GLARE-3/2-0.4 as a baseline, revealing the superior performance of 3DFMLs; the optimal performance was exhibited by 3DFML with a 3 mm 3DC core. However, the lower ductility of 3DFMLs, as well as their poorly controlled and time-consuming fabrication process were recognized. The newly developed materials (IE3DHCs) had a comparatively simpler fabrication processes with significantly higher quality control. More importantly, IE3DHCs exhibited an approximately 160% improvement in ductility and as much as a 250% improved design strength compared to 3DFMLs. These findings showcase the promising potential of IE3DHCs for future research and real-world applications. Additionally, robust finite element models were developed to simulate flexural tests and optimize future renditions of the IE3DHCs.
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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 | 0.001 | 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".