Investigation of the effect of thermal aging and wear test parameters on the wear behavior of glass fiber (GF) reinforced epoxy composites

• Published in: Polymer composites Vol. 45; no. 9; pp. 7820 - 7832
• Main Authors: Sahin, Alp Eren, Yarar, Eser, Bora, Mustafa Ozgur, Yilmaz, Taner
• Format: Journal Article
• Language: English
• Published: Hoboken, USA John Wiley & Sons, Inc 20.06.2024; Blackwell Publishing Ltd
• Subjects: Aging; Coefficient of friction; Composite materials; epoxy; glass fiber; Glass fiber reinforced plastics; Glass-epoxy composites; Regression analysis; Regression models; Sliding; thermal aging; thick composite; Variance analysis; wear; Wear rate; Wear resistance; Wear tests
• ISSN: 0272-8397; 1548-0569
• DOI: 10.1002/pc.28306

This study focused on the wear behavior of unidirectional, thick GF epoxy composites produced using the autoclave method. The thermal aging process was applied for 250 and 500 cycles to understand the wear behavior of the material as it ages over time. To evaluate the wear behavior of the samples, wear tests were carried out under three different loads (10, 20, and 30 N) and three different sliding distances (150, 300, and 450 m). Taguchi‐based regression analysis was used to analyze the experimental results. The results obtained were evaluated using ANOVA tables and main and contour graphs. When the test results are examined, it is surprisingly observed that the wear resistance of the composite material increases positively with the aging effect. With thermally aging while the coefficient of friction value of composite samples increases from approximately 0.12 to 0.3, the wear volume decreases from approximately 4 to 1.3 mm3. Taguchi based regression analysis showed that the relationship between wear and thermal cycle sliding distance and load can be established by reducing the number of experiments in wear studies. Highlights Thermal aging effect on wear properties of thick GFRP was determined. Minimum wear volume and COF values were determined by applying ANOVA. It was determined that a load increment has the most impact on wear volume. Worn surface images supported the reduction in the wear rates of the thick GFRP. Empirical regression models developed for the mathematical prediction. Effect of parameters on wear volume and coefficient of friction. Wear trace image, produced by topography machine.