Tribological behavior of unfilled PTFE under static loading in dry sliding condition: a Taguchi-ANN perspective

Abstract

This work explores the friction and wear characteristics of unfilled polytetrafluoroethylene (PTFE) operating in static unlubricated sliding conditions using Taguchi analysis. The research uses a design of experiment (DOE) technique, focused on sliding velocity, and applied pressure and sliding time as parameters. Systematic experimentation is facilitated with Taguchi’s L9 orthogonal array, and Minitab 17 software is used to evaluate the findings. Signal-to-noise ratios (SNR) are used in the evaluation of individual parameter effects, the creation of regression models, and the establishment of ideal operating conditions. The analysis focuses on predicting wear (W), specific wear rate (Ws), and friction coefficient (f) through regression and ANN (artificial neural network) models, with ANN demonstrating better performance. The results advocate for optimal operating condition for PTFE under static load. This study adds important information for sectors where PTFE is employed as a primary material, such as rolling and sliding contact bearings.