Mechanical performance of a novel environmentally friendly <scp>basalt‐elium</scp>® thermoplastic composite and its stainless <scp>steel‐based</scp> fiber metal laminate
Bibliographic record
Abstract
Abstract A comparative performance study is conducted to better understand the mechanical performance of an environmentally friendly composite and its fiber‐metal laminate (FML) renditions developed with natural fibers (basalt), and a novel room‐cured liquid methyl methacrylate thermoplastic resin (Elium®) and also by a widely used room‐cured epoxy resin (West System). Mechanical characterizations are performed using tensile, buckling and flexural tests. The results indicate that the composites fabricated with the acrylic‐based Elium matrix could be considered as effective alternatives to those produced by thermoset epoxy resins. Moreover, the mechanical properties of a new generation of thermoplastic fiber metal laminates (TP‐FMLs) fabricated by vacuum‐assisted resin infusion technique are also investigated. The TP‐FMLs are fabricated using stainless steel sheets, basalt fabric and Elium. Two different procedures are considered for fabricating the TP‐FMLs; a one‐step process consuming 24 h curing cycle time and a two‐step process with 72 h curing cycle time. The mechanical responses of the developed TP‐FMLs are compared against their equivalent monolithic environmentally friendly basalt‐Elium counterparts, demonstrating the gain in mechanical responses that could be attained by employing the TP‐FMLs.
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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".