Enhancing friction with additively manufactured surface‐textured polymer composites
Bibliographic record
Abstract
Abstract Increasing rubber's friction on slippery surfaces provides protection against falls; however, surface‐textured composites, despite their potential, remain susceptible to wear. To address this issue, part of our team previously patented a surface‐textured composite made from thermoplastic polymers and microfibers. This study investigates the impact of manufacturing processes and 2D filler, which are known for their hydrophobicity and large surface area. It enhances our patented composite by integrating 2D graphene nanoplatelets (GNP), hexagonal boron nitride (hBN), and fillers like styrene‐butadiene‐styrene (SEBS), and silica, while comparing the properties of composites fabricated via injection molding (IM) and fused filament fabrication (FFF). The results demonstrate that 2D fillers enhance both abrasion resistance and ice friction, while FFF‐fabricated composites consistently exhibit superior properties across all compositions. Notably, hBN‐reinforced samples exhibited hierarchical surface texturing, leading to enhanced abrasion resistance (FFF: 146.63% ± 3.39%; IM: 133.83% ± 6.8%; p = 0.036), and effective ice traction (FFF: 0.58 ± 0.04; IM: 0.54 ± 0.06; p = 0.043). These outperformed ice‐traction properties of all other FFF‐fabricated composites, including a previously patented composite (0.52 ± 0.05) as well as composites with GNP (0.53 ± 0.02), SEBS (0.42 ± 0.05), and hBN + SEBS (0.45 ± 0.02). Additionally, the patented composite produced via FFF exhibited moderate oil traction (0.121 ± 0.001), outperforming others. This study highlights the potential of FFF and 2D fillers to enhance traction and durability in composites. Highlights Surface‐textured composite introduced via additive manufacturing. Abrasion resistance and friction analysis on icy and oily conditions. Reveals the potential for new composite to improve traction and longevity. Highlights the importance of controlled fiber distribution and orientation.
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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 | 0.000 | 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) | 0.001 | 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".