Adhesive wear characteristics of mono and hybrid CF/Ep composite with nano-HAP filler

Divya  GURKAR SOMASHEKAR; Naveena  BETTAHALLI ESWAREGOWDA; Suresha  BHEEMAPPA

doi:10.55713/jmmm.v34i3.2040

Authors

Divya GURKAR SOMASHEKAR Department of Automobile Engineering, Dayananda Sagar College of Engineering, Bangalore-560078, Karnataka, India
Naveena BETTAHALLI ESWAREGOWDA Department of Automobile Engineering, Dayananda Sagar College of Engineering, Bangalore-560078, Karnataka, India
Suresha BHEEMAPPA Department of Mechanical Engineering, The National Institute of Engineering, Mysuru-570008, Karnataka, India

DOI:

https://doi.org/10.55713/jmmm.v34i3.2040

Keywords:

Hybrid composites, Taguchi technique, Hydroxyapatite, Carbon fiber, Epoxy, Two-body adhesive wear

Abstract

Composites materials with more than two reinforcing materials are called hybrid composites. Tailoring the composites by hybridizing fillers, fibers and matrix will yield better properties compared to mono-composites. Hence, an effort has been made in the current research work to develop carbon fiber epoxy hybrid nanocomposites, comprising different weight percentage of Hydroxyapatite (HAP) to evaluate the potential effects on tribological properties using two body sliding wear method. Taguchi technique (L₂₇ array) has been adopted to investigate the impact of parameters such as filler inclusion (0%, 1.5%, and 3%), load (30, 45, and 60 N), sliding velocity (1, 2, and 3 m·s^‒1) and distance (1000, 2000, and 3000 m·s^‒1) on wear loss of developed composite. It was observed that the combination of 1.5 wt% HAP composite showed the lowest Ks and the COF. The combination of 1.5 wt% HAP filler, 1 m·s^‒1sliding velocity, 45 N load and 3000 m sliding distance exhibited the lower Ks and COF of 0.44652 × 10^‒14 (m³·Nm^‒1) and 0.136 respectively. The significance of the parameters was assessed using analysis of variance, revealing that the filler's contribution significantly impacted wear resistance. Developed mathematical model using Regression analysis and the predicted values from K-Nearest Neighbors (KNN) have showed good agreement with experimental values. Micrograph images were captured to analyze the wear mechanisms evident on worn surfaces, revealing failure mechanisms such as extensive matrix damage, fiber exposure resulting from matrix removal, and fiber breakage.

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