Stojanović, B., Vencl, A., Bobić, I., Miladinović, S. and Skerlić, J. (2018) Experimental Optimisation of the Tribological Behaviour of Al/SiC/Gr Hybrid Composites Based on Taguchi’s Method and Artificial Neural Network. Journal of the Brazilian Society of Mechanical Sciences and Engineering, 40, Article No. 311.
https://doi.org/10.1007/s40430-018-1237-y

has been cited by the following article:

• TITLE: Optimization of Wear Factors of Aluminium Hybrid Metal Matrix Composites Using Taguchi Method

  AUTHORS: P. B. Asha, Sneha Sarika Murthy, C. R. Prakash Rao, V. Ravi Kumar, R. Kiran, C. N. Suresha

  KEYWORDS: Taguchi, ANNOVA, Wear, Signal-to-Noise Ratio, Hybrid Metal Matrix Composite

  JOURNAL NAME: Journal of Minerals and Materials Characterization and Engineering, Vol.9 No.1, January 14, 2021

  ABSTRACT: Hybrid metal matrix composites (Hmmc) are found to be more superior than the conventional composite materials because of their improved mechanical properties, which can be suited for an extensive range of engineering applications. Automobile and aerospace industries widely make use of hybrid composites as they possess excellent corrosion, wear resistance, low density, and high strength. This paper displays the strategy to build the hybrid composite utilizing Stir casting Method. Present investigation includes the creation of composites utilizing boron carbide (2%, 4%, 6% volume) and Red mud (2% volume) as the reinforcements and Structural aluminium as the matrix. Experimental investigation of wear analysis of the composites was carried out according to the L9 Taguchi method. The designated number of experiments was accomplished to probe the impact of control factors on the specific wear rate (SWR) of the developed composites. ANOVA was carried out and Wt%. Reinforcement was found to be the decisive factor on the SWR of the developed hybrid composite. The Confirmatory test was successfully carried out and the computed error was found to be varying from 0.878% to 2.58%.