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

IF 3.4 Q2 MATERIALS SCIENCE, MULTIDISCIPLINARY
Kiran Ashokrao Chaudhari, Jayant Hemchandra Bhangale
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引用次数: 0

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.

干滑动条件下静载荷下未填充聚四氟乙烯的摩擦学行为:田口神经网络视角
本研究利用田口分析方法研究了未填充聚四氟乙烯(PTFE)在静态无润滑滑动条件下的摩擦和磨损特性。研究采用实验设计(DOE)技术,以滑动速度为研究对象,以压力和滑动时间为参数。使用田口L9正交阵列进行系统实验,并使用Minitab 17软件对结果进行评估。信噪比(SNR)用于评估单个参数的影响、建立回归模型和建立理想的操作条件。分析重点是通过回归和人工神经网络模型预测磨损(W)、比磨损率(Ws)和摩擦系数(f),其中人工神经网络模型表现出更好的性能。结果表明,在静载荷作用下,聚四氟乙烯的最佳操作条件。这项研究为采用聚四氟乙烯作为主要材料的部门增加了重要信息,例如滚动和滑动接触轴承。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
8.60
自引率
0.00%
发文量
1
审稿时长
13 weeks
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