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Journal of Polymer & Composites Review Article Special issue

A Review on Predicting Wear and Friction of PTFE Composites - Fillers to Machine Learning Models

  1. Abhijeet R. Deshpande Department of Mechanical Engineering, Vishwakarma Institute of Information Technology, Pune
  2. Atul Kulkarni Department of Mechanical Engineering, Vishwakarma Institute of Technology, Pune
  3. Prashant Anerao Department of Mechanical Engineering, Vishwakarma Institute of Technology, Pune
  4. Swapnil Shah Department of Mechanical Engineering, Vishwakarma Institute of Information Technology, Pune
  5. Pranav Kulkarni Department of Mechanical Engineering, Vishwakarma Institute of Information Technology, Pune

Abstract

Polytetrafluoroethylene (PTFE) composites, a self-lubricating material with low friction, became an indispensable material in engineering applications where load carrying capacity and wear are crucial. The pure PTFE has poor mechanical strength and wear resistance which can be enhanced by the addition of fillers in appropriate volume fraction. The wear performance is dependent on various factors such as fillers, operating parameters, environmental conditions as well as manufacturing attributes. This makes the analysis of any tribological system more complex with nonlinear interactions with different influencing elements. This limitation focuses the need of adopting a data driven machine learning (ML) approach to provide more accurate understanding of the tribo-system and provide more accurate prediction of wear.ML techniques have emerged as an effective tool for understanding wear mechanisms and accurately predicting the wear rate and coefficient of friction (COF). These ML algorithms are data driven, learn from the experimental data finds the trends in material composition with different fillers and operating circumstances for accurate forecasts. Gradient boosting model (GB) shown high predictive accuracy (R² values up to 0.95) for PTFE composites, outperforming traditional models by capturing non-linear interactions and adapting to varied conditions. ML techniques offer interpretability, critical for understanding the impact of each material parameter as well as providing robustness with smaller datasets, making it suitable for applications where data availability is limited. These models provide stability and accuracy in forecasting wear behaviour, which is essential in real-world applications where complex material interactions are present This review identifies current challenges, such as data quality and model validation, and emphasizes the need for hybrid models that combine the strengths of ML and numerical methods. Future research should focus on expanding the predictive capability of these models through more comprehensive datasets and advanced algorithms, aiming for sustainable, cost-effective, and high-performance tribological solutions in PTFE composite applications across automotive, aerospace, and medical industries

Keywords

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