Predictive modelling of graphene-enhanced greases using classical feedback control and quantum kernel regression
Bibliographic record
Abstract
This paper investigates two predictive modeling approaches for estimating the thermal and tribological performance of graphene-enhanced greases, aiming to reduce reliance on protracted endurance tests. Seven grease formulations with varying graphene concentrations (0–4 wt%) were prepared and tested under a uniform load to capture temperature evolution, wear scar area and coefficient of friction. A classical piecewise regression model, augmented by a Linear Quadratic Regulator (LQR), leverages feedback control to correct temperature predictions and subsequently estimate wear using a polynomial fit. This framework demonstrated high accuracy in tracking transient thermal behaviour, maintaining temperature deviations within ±1 °C of measured data. In parallel, a quantum-classical hybrid model employs a fidelity-based quantum kernel with support vector regression. By encoding partial early-cycle temperature measurements (e.g., from 30 to 120s) into a higher-dimensional Hilbert space, the quantum approach captures subtle nonlinearities and yields strong correlations for both final temperature and wear scar area. Moreover, consistent performance on IBM Quantum models with realistically simulated noise underscores the model’s potential for practical industrial implementation. Collectively, these results confirm the viability of advanced computational tools, both classical and quantum, for rapid, data-driven lubricant assessments. They highlight opportunities to optimize graphene content while minimizing costly trial and error testing.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".