Open Access

Tribological Optimization of Al/ZrO₂ Nanocomposites: Influence of Reinforcement Content on Wear Performance Using Taguchi Method

T. Shaafi, shaafit.sse@saveetha.com
Department of Mechanical Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences (SIMATS), Chennai, TN, India
M. Kalaimani, mkalaikvn@gmail.com
Department of Mechanical Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences (SIMATS), Chennai, TN, India
M. Yuvaperiyasamy, yuvaperiyasamyvsb@gmail.com
Department of Mechanical Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences (SIMATS), Chennai, TN, India
Krishna Prakash Arunachalam, k.prakash@utem.cl
Departamento de Ciencias de la Construcción, Facultad de Ciencias de la Construcción Ordenamiento Territorial, Universidad Tecnológica Metropolitana, Santiago, Chile
M. Karthigairajan m.karthigairajan@gmail.com
Department of Mechanical Engineering, Karpaga Vinayaga College of Engineering and Technology, Chengalpattu, TN, India



J. Environ. Nanotechnol., Volume 14, No 2 (June 2025) pp. 162-177

https://doi.org/10.13074/jent.2025.06.2511453

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Abstract

This study investigates the tribological behaviour of aluminium matrix nanocomposites reinforced with Zirconium dioxide (ZrO₂) nanoparticles, synthesized using the powder metallurgy method. Three different compositions were prepared: AlZ3 (3 wt.% ZrO₂), AlZ6 (6 wt.% ZrO₂), and AlZ9 (9 wt.% ZrO₂), with aluminium as the base matrix. The mechanical mixing was performed using a high-energy ball mill, followed by compaction and sintering in a controlled environment. Wear performance was evaluated using a pin-on-disk tribometer under varying loads and sliding distances. The Taguchi method using an L9 orthogonal array was employed to design the experiments and determine the optimal combination of normal applied load (NAL), sliding distance (SD), and ZrO₂ content for minimizing wear rate, friction coefficient, and specific wear. Additionally, the ANOVA method was employed to identify the key factors influencing wear resistance and to evaluate the significance of their effects. Regression analysis is used to model and predict the relationship between process parameters and better wear resistance. It quantifies the influence of each factor, enabling accurate optimization of conditions for improved wear performance. Taguchi analysis reveals that the optimal conditions are a normal applied load of 30N, sliding distance of 1000 m and 6 wt.% of ZrO2 nano reinforcement. The R² values for wear rate 84.58%, friction factor 87.79%, and specific wear 81.51% indicate strong model fits. These results confirm that the regression models effectively capture the variability in wear performance metrics.

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Reference


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