Characterization of curing status of commercial tire compounds with vein graphite powder and its particle sizes—Experimental and computational study
Bibliographic record
Abstract
Abstract The use of solid tires in demanding industrial applications, subjected to substantial mechanical loads, poses significant challenges due to heat generation from hysteresis and tread friction. Managing this heat is crucial to mitigate the risk of tire blowouts and layer separation. This research investigates the impact of introducing vein graphite powder, varying in particle size, into a commercially used solid tire compound, both experimentally and computationally. The research findings presented in this paper reveal substantial changes in thermal conductivity, specific heat capacity, rate constant, induction time, and the order of reaction in solid tires. Contemporary industry trends involve predictive methods for monitoring the curing process in tire manufacturing. A tire curing simulation model based on finite (FE) element analysis is developed. FE modeling is favored due to its accuracy and adaptability, especially when dealing with the intricate geometry and multi‐layered, multi‐compound structure of tires. The complex interplay between heat transfer and curing processes is effectively addressed using user subroutines (UMATHT) in commercial FE software like ABAQUS. In this analysis, thermal conductivity, heat capacity, order of reaction, rate constant, and induction time of the commercial tire compound are considered as temperature‐dependent variables. The computational model not only demonstrates its potential to significantly enhance the efficiency and quality of tire manufacturing processes but also contributes to a deeper understanding of the curing behavior of graphite powder‐based solid tire compounds. Consequently, it provides a valuable tool for optimizing tire manufacturing procedures and ensuring the safe and reliable performance of solid tires under demanding operational conditions. This research bridges the gap between the traditional commercial tire compound and the use of vein graphite powder in tire manufacturing and advanced computational methods, facilitating improvements in tire quality and safety.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".