Optimum Design of a Composite Helical Spring by Multi-criteria Optimization
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
A new methodology for the optimum design of composite helical springs with braided fibrous reinforcement is presented in this article. A multi-objective evolutionary algorithm is implemented to optimize two conflicting goals: minimize mass and maximize stiffness. Several design variables that have an influence on the mechanical properties of the spring must be considered: the braiding angle, number of plies and the standard design parameters of a helical spring. Design goals are set such as for standard metallic springs: equivalent mechanical performance, mass reduction, and comparable cost. Three different braided reinforcements in carbon, kevlar, and glass were analyzed with the same epoxy matrix. In helical springs, shear plays the most important role on spring performance. Taking into account the shear properties of braided composites and a series of technological constraints, a range of composite springs was devised, among which an optimal spring was selected for an automotive application, namely to replace the metallic spring of the suspension of a sport utility vehicle.
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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 | 0.000 | 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) | 0.000 | 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 it