Viscoelastic tribopairs in dry and lubricated sliding friction
Bibliographic record
Abstract
Soft contacts present different tribological responses compared to stiff materials, especially when soft materials exhibit viscoelastic behaviour, as viscoelastic materials have intermediate mechanical properties between viscous liquids and elastic solids. In this work, we investigated the influence of viscoelasticity of soft materials on sliding friction in dry and lubricated conditions. To achieve this, soft tribopairs with varying viscoelasticity were obtained by tuning the weight ratios of polydimethylsiloxane (PDMS) base and curing agent. The real-time friction force and preload were observed over multiple conditions, with systematic control of lubricant viscosity, preload, and sliding velocity. Tribopairs with a higher proportion of viscous character had more oscilliations in the friction force. They also presented a higher friction coefficient due to the increased contribution of viscoelastic hysteresis losses on friction. Through regression analysis, the models of the friction coefficient were found, which are in good agreement with experimental results. From the models, we found that in both dry and lubricated conditions, viscoelasticity of tribopairs, indicated as the loss modulus or loss tangent, plays a key role in determining the friction coefficient. This influence is particularly significant for dry contacts due to the direct interactions between surfaces of tribopairs. This study provides empirical proof and a focused analysis on the role of viscoelasticity in tribological contacts.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".