Design optimization of an automotive universal joint considering manufacturing cost
Bibliographic record
Abstract
This paper presents the shape optimization of an automotive universal joint, by simultaneously considering manufacturing cost, maximum drivable joint angle and part volume. Comprised of three main components - two yokes and a cross trunnion - a universal joint is a linkage used to transmit rotational motion from one shaft to another when the axes are coplanar, but not coinciding. In this research, universal joint designs are analyzed and compared using a weighted sum of three objective functions: minimization of machining cost, maximization of adjoining shaft joint angle, and minimization of total part volume. Part modeling and analysis is conducted using the Finite Element Analysis package ANSYS and optimization is implemented using MATLAB. The results show Pareto frontiers for both the flange and weld yoke, constructed using the Adaptive Weighted Sum technique. These frontiers clearly illustrate the trade-off between machining cost and joint angle; that is, to increase the joint angle, a corresponding increase in the cost of the part is required. It has been shown that maximization of driveable joint angle requires a simultaneous increase in machining cost of 4.4% and 2.7% for the flange and weld yoke, respectively.
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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 | 0.001 |
| 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) | 0.001 | 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".