Optimization of friction, wear and temperature in graphite-reinforced PTFE/bronze composite bearings using Taguchi analysis

Abstract

This study aims to develop and optimize a self-lubricating porous plain bearing system fabricated through powder metallurgy and surface spray coating of polytetrafluoroethylene (PTFE) and graphite-reinforced PTFE composites. The research focuses on minimizing friction coefficient, wear loss and bearing operating temperature under dry sliding conditions. The objective is to statistically evaluate the influence of material composition, applied load and sliding velocity on tribological performance and to determine the optimal parameter combinations using a systematic design framework. Porous CuSn11 bronze bushings were manufactured using powder metallurgy and subsequently coated with neat PTFE, PTFE + 10 Wt.% graphite and PTFE + 20 Wt.% graphite. Tribological experiments were conducted under dry sliding using a plain bearing test rig based on a Taguchi L27 orthogonal array design. Signal-to-noise ratio analysis and analysis of variance (ANOVA) were applied to evaluate parameter significance and interaction effects. Confirmation tests validated optimal conditions, while SEM and EDS analyses were performed to characterize wear mechanisms and transfer film formation. Material composition was identified as the dominant factor affecting friction coefficient (71.61%) and wear loss (30.88%), whereas sliding velocity primarily governed bearing temperature (55.06%). PTFE reinforced with 10 Wt.% graphite exhibited the lowest friction, wear and temperature under optimized conditions. Statistical validation demonstrated strong agreement between predicted and experimental results (>99.5% confidence). SEM analysis confirmed abrasive wear as the predominant mechanism, while graphite reinforcement promoted stable tribofilm formation, significantly enhancing tribological performance under dry sliding conditions. This research presents a hybrid bearing architecture integrating powder metallurgy-based porous bronze with spray-deposited graphite-reinforced PTFE coatings, optimized using a Taguchi–ANOVA statistical framework. Unlike conventional tribological studies, this work quantitatively establishes parametric hierarchy and interaction effects with high statistical confidence. The findings provide practical design guidelines for advanced self-lubricating bearing systems and contribute to manufacturing-driven performance optimization strategies relevant to rapid prototyping and engineered composite surface technologies. The multi-response engineering trade-off framework adopted in this study is methodologically aligned with recent Taguchi-based optimization approaches reported in advanced manufacturing (Boulahem et al., 2026), further contextualizing this study within the Rapid Prototyping Journal scope.