Holistic design and software aided finite element analysis (FEA) of an All-Terrain Vehicle
Bibliographic record
Abstract
We have tried to design an All-Terrain Vehicle that meets international standards and is also cost effective at the same time. We have focused on every single system to improve the performance of each component. Our vehicle can navigate through almost all terrain, which ultimately is the objective behind the making of any All-Terrain Vehicle. We began the task of designing by conducting extensive research of each main component of the vehicle. We did not want to design certain areas such as the frame, and then make the rest to fit. We considered each component to be significant, and thereby designed the vehicle as a whole trying to optimize each component while constantly considering how other components would be affected. This forced us to think outside the box, research more thoroughly, and redesign components along the way in order to have a successful design. Combining this design methodology with the standard engineering design process enabled us to achieve a perfect match of aesthetics, performance, and ease of operation. Our current design is reliable and stable. Extensive research helped us pinpoint the needs of the customers which helped us to refine our design further. We used the necessary parameters to create a Qualitative Function Diagram (QFD) to determine which parameters were the most critical. These key parameters ranging from most critical to least critical are safety, reliability, low cost, ease of operation and maintenance, and overall performance. Key words: Power train, final-drive, rack and pinion, suspension, brakes, roll cage, double Wishbone suspension.
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 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".