Design of a Mono-Leaf in Sandwich Structure for Application in Light-Load Vehicle Using Finite Element Method
Bibliographic record
Abstract
<div class="section abstract"><div class="htmlview paragraph">This paper aims to present the design of a mono-leaf in steel/composite sandwich structure (epoxy/glass fiber). The automotive main challenges now-a-days are fuel economy and CO2 emission reduction. To achieve such goals, the usage of new materials and design optimization procedures are required. This research focuses on light-load commercial vehicles, in special, rear suspension. Leaf springs are the key components of such suspension. Therefore, the design optimization procedure developed is centered into leaf spring weight reduction. The design optimization procedure was bounded by industry regulatory standards and base on analytical and numerical experiments. Once the design phase is completed, a finite element analysis was performed using ANSYS Workbench<sup>®</sup>. The finite element analysis not only provided a detailed mapping of stress and displacement fields, but it also allowed to identify possible regions of failures. Moreover, it is possible to make a comparison among all the cases studied. The final step is the fuel economy analysis.</div></div>
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".