Abstract
This study investigated the effect of nano-zeolite (NZ) integration on the tribological properties of polyamide 6 (PA6) nanocomposites using a hybrid approach combining image segmentation, experimental techniques, and numerical modeling. Automated SEM image analysis with U-Net segmentation quantified nanoparticle distribution, while pin-on-disc tests assessed wear performance. A support vector regression (SVR) model linked nanoparticle dispersion, load, and hardness to friction and wear rates. A combined dispersion index (CDI) further quantified nanoparticle distribution. Results demonstrated significant wear rate reductions (41 % at 60 N and 54 % at 100 N) with 5 wt% NZ. The SVR model highlighted the importance of nanoparticle dispersion and load on performance, with hardness having minimal influence. Morphological analysis confirmed smoother worn surfaces with NZ inclusion.
Original language | English |
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Article number | 110455 |
Journal | Tribology International |
Volume | 204 |
DOIs | |
State | Published - Apr 2025 |
Keywords
- Computer vision
- Image segmentation
- Polymer nanocomposite
- Wear