Sliding Wear Behavior of Pineapple Leaf/Glass Fiber Reinforced Polyester Composites

In this work, the effect of the kind of reinforcement on the sliding wear performance of a polyester matrix composite reinforced with glass fiber and pineapple leaf fiber is investigated. The purpose of this study is to ascertain the effects of the type of reinforcement on the sliding wear performance of a polyester matrix composite reinforced with glass fiber and pineapple leaf fiber. By using hot press molding and the 30% weight fraction of fiber reinforcement, polyester composites with four distinct compositions-PPP, GGG, PGP, and GPG-have been created. These composites' dry sliding wear experiment is carried out using a Pin on Disc wear testing device. The following parameters are used in experiments: a track radius of 50 mm, a sliding distance (SD) of 500 m, an applied load of 50, 70, or 90 N, and a sliding velocity of 3.14, 4.71, or 6.28 m s-1. For dedicated (PPP, GGG) and hybrid (PGP, GPG) polyester composites, the effects of load, sliding velocity (SV), and wear rate (WR) are studied. At 50 N and 3.14 m s-1 the COF for PPP and GGG dedicated composites is 0.68 and 0.70, respectively, whereas for PGP and GPG hybrid composites, it is 0.41 and 0.64. The PGP/GPG hybrid composites have a minimal wear rate of 3.34e-8 m3 s-1 and are 80% more effective than dedicated composites. Utilizing the Analysis of Variance (ANOVA) and Taguchi L9 orthogonal array, regression models are created to optimize the influence of various parameters, such as load, sliding velocity, and composition. Results of confirmation tests show excellent agreement with predictions generated using the developed mathematical model.

Automated summary

This study investigates the effect of reinforcement on the sliding wear performance of a polyester matrix composite reinforced with glass fiber and pineapple leaf fiber. The results show that hybrid composites have lower coefficients of friction and wear rates compared to dedicated composites. Mathematical models are used to optimize the influence of various parameters.