The purpose of this study is to utilize microwave-assisted compression molding (MACM) to fabricate HDPE/CNT and PP/CNT nanocomposites, evaluate their mechanical and tribological properties, and develop a finite element model to predict their wear rate. MACM was used to synthesize polymer nanocomposites with varied CNT volume fractions (5% and 10%). Mechanical properties, including hardness, tensile strength, elastic modulus and flexural strength, were assessed through shore D hardness, tensile test and flexural test. Pin-on-disc wear tests were performed to evaluate tribological performance, and scanning electron micrographs were examined to characterize the worn-out surfaces of nanocomposites. A computational FE model was developed to simulate wear behavior, and the results were compared to experimental data.
MACM is a novel method for producing high-performance polymer nanocomposites. The objective of this investigation is to use MACM to create HDPE/CNT and PP/CNT nanocomposites, evaluate their mechanical and tribological characteristics and establish a FE model to forecast their wear rate.
The wear resistance, hardness and mechanical properties of HDPE and PP nanocomposites were markedly improved by the incorporation of CNTs. The coefficient of friction for HDPE containing 10% CNT decreased to 0.14 when subjected to a 30 N load. The HDPE/CNT nanocomposites demonstrated hardness enhancements of 9.1% and 18.2% for 5% and 10% CNT, respectively. CNT-reinforced nanocomposites exhibited cleaner worn surfaces as a result of scanning electron microscope analysis. The computational analysis confirmed the FE models’ reliability, which was in good agreement with the experimental results.
This research emphasizes the utilization of MACM in the fabrication of HDPE/CNT and PP/CNT nanocomposites, demonstrating their exceptional mechanical and tribological properties. The study advances the broader comprehension of polymer nanocomposite behavior in high-durability and low-friction applications by introducing a validated FE model for wear rate prediction.
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