Bibliographic record
Abstract
Considering shapes in designs does not only end in aesthetics and streamlining in aerodynamics, but also is a consideration of structural effects on rigidity, strength propagation in absorbing both the external and internal stresses due to the prevailing factors like impact, vibration, dynamic and static pressures.Streamlining is shelving the external pressures as to maximize fuel economy through loss or reduction of drag.The automotive main leaf spring is elliptical having two eyes characterized with width (w), thickness(t), span (l), radius of curvature (r) and made of steel material.A correlative analysis is conducted on these parameters using Pearson correlation coefficient(C) under a load (P) of 0.5KN by the application of wellknown versatile Hooke's law of elasticity with application of Solid Works.Two CAE knowledge based softwares -Creo Element and Ansys were utilized in this analysis to validate the results obtained from SolidWorks and statistical analysis.The statistical tool-Pearson correlation coefficient was employed and it revealed a positive correlation analysis of the Correlation analysis carried out with the simulation data, produced Correlation coefficients of 0.997539666, 0.988660899, 0.994500728,0.99721782,0.996183593 & 0.999965163; showing a very strong correlation between the variables.Since the Correlation coefficient obtained is greater than the corresponding critical value of Correlation coefficients, at 5% and 1% changes which were 0.878 and 0.959; respectively, (under 0.1 tailed test of type1 error) for the correlation degree of freedom of 3, it follows that this research runs only 1% chance of being wrong in the relationship, so it is determined.Hence, it will be useful in leaf spring design.Results from Ansys Workbench also shows that geometric parameters have influenced on the performance of the leaf spring with the distinct values of displacement(𝛿𝛿), maximum principal stress and maximum Von Mises stress.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.955 | 0.969 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".