Development of an Engineering Analysis Tool for Time-Temperature Analysis of Automotive Components
Bibliographic record
Abstract
<div class="htmlview paragraph">This paper describes the development of an engineering analysis tool that assesses the life of vehicle components, after exposure to heat.</div> <div class="htmlview paragraph">As a standard engineering practice, each component or part of a component has a “long term” and a “short term” temperature goal based on the part’s material physical properties. At higher temperatures, component’s physical properties degrade at a faster rate, and the component’s useful life can be significantly reduced. The extent of degradation depends upon the duration of exposure, the magnitude of the over-temperature and rate of thermal degradation.</div> <div class="htmlview paragraph">This tool utilizes actual vehicle test data from test cells or road testing, material physical properties, and expected vehicle duty cycle to determine the expected component life. When component temperature goals are exceeded, the software calculates the total duration of time above the goal temperature.</div> <div class="htmlview paragraph">Kinetic degradation models [<span class="xref">1</span>–<span class="xref">2</span>] (which utilize the material’s activation energy value and Arhenius’ kinetic model) are used to calculate the component’s Equivalent Exposure Time (<i>EET</i>) using <span class="xref">equation (1)</span> at each temperature over-goal. The model then utilizes the component’s thermal duty cycle (based on the vehicle’s operating duty cycle) and the calculated (<i>EET</i>) values to calculate the component’s total thermal exposure during the vehicle’s lifetime (150,000 miles). The tool then uses these results for a final (Pass or Fail) assessment of the component.</div>
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.026 | 0.012 |
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".