Vector Load Simplified Duty Cycle for Lower Control Arm
Bibliographic record
Abstract
Multi-axial loaded parts like lower control arms are routinely tested in the laboratory for durability verification. But the full anticipated complex road load data is not normally applied because it would be too expensive and complex to test all parts this way. Instead, a simplified loading condition is used. Ideally, this will be as simple as a single sine wave loading applied to one loading point and in one direction. The specification of which hard point to use, which loading direction, and which frequency and load magnitude, requires very good engineering judgement and a high degree of experience. Even then, it is unlikely that the optimum solution will be obtained and the risk of creating a non-representative test is high. Recently, a new FEA technique has been developed which simultaneously derives both an optimum loading profile (surrogate load) and loading direction (vector direction) from full Proving Ground [PG] real loading conditions. This paper will present the technique that was developed in collaboration with CAEfatigue Ltd. and shows results for a realistic LCA example. The model used is only statically responsive but the approach outlined is valid for both static and dynamically responsive models.
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.033 | 0.005 |
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".