DURABILITY TESTS: statistical analysIs for variable amplitude loads
Bibliographic record
Abstract
The reduced time available for product evolution has forced original equipment manufacturers and their suppliers to develop new components and subsystems more rapidly, while taking into consideration the reliability of the final product in terms of its durability. Although durability is nowadays improved through virtual testing, it is mandatory to perform experimental tests for the final release of a product. The complexity of this kind of testing has increased. In the early 20th century, evaluations began to represent a real life time history with standardized load–time histories. This coincided with the introduction of the closed-loop test system to reproduce more realistic time histories. Most tier-1 suppliers perform such testing at their own development centers or at consultant test facilities; however, design engineers and product management need to understand the requirements of the testing and be able to interpret the results to understand how to improve a mechanical component. Most papers published on durability give experimental results for constant-amplitude loads and analytical predictions. However, information on tests with time histories and statistical analysis is very often missing. The present paper reviews the main aspects of durability tests for variable-amplitude loads, including the main statistical analysis conducted to reproduce damage to vehicles traveling on roads and proving grounds in the laboratory.
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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 | 0.015 | 0.071 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".