On Similarities and Differences of Measurements on Inertia Dynamometer and Scale Testing Tribometer for Friction Coefficient Evaluation
Bibliographic record
Abstract
Inertia dynamometers are commonly used to determine the friction coefficient of brake assemblies. Dynamometers are a well-established platform, allow testing under controlled conditions, exhibit a good correlation to many situations encountered in real driving, and are comparatively economical and less time-consuming than full vehicle test. On the other side of the spectrum is the use of scaled tribometer. These test systems make possible a test without the entire brake corner. This separation allows the investigation of the frictional-contact only (frictional boundary layer) speedily and independently of a given brake system or vehicle configuration. As the two test systems (inertia dynamometers and tribometers) may have different users with possibly different tasks, the question remains regarding how comparable the two systems are. These issues provide incentives to better define the fields of investigations, correlation, and applicability for the two systems. In order to provide further insights and learning on this topic, this paper focuses on the measurement of the friction coefficient and the wear behavior using inertia dynamometer and scaled pin-on-disc tribometer testing. Thus, friction coefficient levels and sensitivity are investigated on both systems when using a tribometer based test sequence (test with constant speed) as well as using a standard inertia dynamometer test procedure (with dynamic braking with in-stop deceleration). Different types of lining-materials (e.g. low-met or OES, and NAO or RE) are tested and compared. One major difference between the two systems is the energy input and thus the different temperature regimes. Therefore the influence of the initial temperature is a critical item the paper focuses on. Lastly, the paper presents potential areas for integration with the objective of improving the overall correlation between the two methods and the ability to use a scale tribometer to predict full inertia dynamometer results for friction and wear evaluations during the product life-cycle.
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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 | 0.005 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".