ArticleScientific reports2022
Optimization and prediction of tribological behaviour of filled polytetrafluoroethylene composites using Taguchi Deng and hybrid support vector regression models.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
3 citing papers in PubMed.
- Wear behaviour and statistical assessment of organomodified nanoclay reinforced glass fiber epoxy nanocomposites.Scientific reports · 2025Article
- Parametric optimisation for the design of gravity energy storage system using Taguchi method.Scientific reports · 2022Article
- Anti-Wear and Anti-Erosive Properties of Polymers and Their Hybrid Composites: A Critical Review of Findings and Needs.Nanomaterials (Basel, Switzerland) · 2022Review
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This study presents optimization and prediction of tribological behaviour of filled polytetrafluoroethylene (PTFE) composites using hybrid Taguchi and support vector regression (SVR) models. To achieve the optimization, Taguchi Deng was employed considering multiple responses and process parameters relevant to the tribological behaviour. Coefficient of friction (µ) and specific wear rate (K
Identifiers
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Registered trials
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