Optimum Design of a Composite Helical Spring by Multi-criteria Optimization
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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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".