The Amount of Excess Weight from the Design of an Armored Vehicle Body by Using Composite Materials Instead of Steel
Bibliographic record
Abstract
In this paper, the amount of excess weight resulting from the design of a mathematical model composed of composite materials will be calculated and compared with a mathematical model of an armored steel structure. Five different models were designed, one of which is made of steel, the other part is made of composite materials, and a section of steel and composite materials, and then tested for resistance to stresses and compared the weight of each structure with that of the steel component by taking the maximum stress as a basic criterion for weight comparison. The results showed that the best model was the second model fiberglass, where the percentage of weight loss was compared to the steel model (73.77%), in addition to the wall thickness (62 mm) and the wall thickness of the steel model with which the comparison was (60 mm), but the displacement is (7. 24 mm), and in the steel model it is (1.827 mm). The best model compared to steel in terms of resistance to maximum stress, less displacement and less weight was the model consisting of steel with carbon fiber and its thickness was (47 layers& 57 mm, 2 layer & 10 mm steel and 45 layer & 45 mm carbon fiber), and the percentage of weight loss compared to the first mathematical model (60.96%). The results of this research may be a key to obtaining alternative materials for traditional materials in the manufacture of armored hulls, aircraft and ships, and it has a lower weight.
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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) | 0.001 | 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 | 0.001 |
| 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".