Static and Vibration Analysis of an Aluminium and Steel Bus Frame
Bibliographic record
Abstract
The transport sector is increasing day by day to satisfy the global market requirement. The bus is still the main mode of intercity transportation in Canada. Despite, an essentially unchanged conception, the total weight of the bus has increased by over 25% during the last three decades. To solve this problem, industrialists have moved to the use of light metals in the transportation field. Therefore, use of lightweight materials, such as aluminum is essential to reduce the total weight of bus. In this study, the focus is on the bus frame as it represents 30% of the total weight and it is the most stressed part of the bus. Its life duration is more important compared to that of all other elements. Thus, a study of the static and vibratory behavior would be very decisive. In this article, two types of analysis are carried out. First is the modal analysis to determine the natural frequencies and the mode shapes using a developed dynamic model of the bus. Because if any of the excitation frequencies coincides with the natural frequencies of the bus frame, then resonance phenomenon occurs. This may lead to excessive deflection, high stress concentration, fatigue of the structure and vehicle discomfort. In this case, the results analysis shows that the natural frequencies are not affected by the change of material. The second type of analysis is the linear static stress analysis to consider the stress distribution and deformation frame pattern under static loads numerically. For the numerical method, the frame is designed using SolidWorks and the analysis is made using Ansys WorkBench. The maximum Von Mises stress obtained for the static loading is in the same order for the three chassis frames studied. But in the case of the aluminium frame, the weight of 764 kg was reduced.
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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".