Tribological performance evaluation of h-BN nanoparticle reinforced AA 7075 and as-cast AA7075 using Taguchi and genetic algorithm

Abstract

In addition to the properties of reinforcement, the tribological parameters (temperature, applied load, and sliding distance) play a vital role in determining the tribological characteristics of the composites. Though there are studies related to the AA7075/h-BN composites, the influence of tribological parameters on the friction and wear characteristics is not addressed. In this regard, a solemn attempt has been made in this research work to study the role of tribological parameters on the tribological characteristics of AA7075/h-BN composites. Taguchi method and genetic algorithm are employed to predict the impact of tribological parameters on the wear resistance of AA7075 and AA7075/h-BN composites. The formulation of genetic algorithm models along with regression analysis is used for estimating the effect of each input parameter on the tribological behaviour of the AA7075 MMCs. The microstructure of AA7075/h-BN composite is analysed and found that the formation of rosette-like dendrites with increased mechanical and tribological properties. The optimum wear rate and Co-efficient of Friction (CoF) values of AA 7075/h-BN composite is obtained with input process parameters at a temperature of 30 °C, applied load 10 N, and sliding distance of 1000 m respectively. The further results show that the temperature and load play a vital role in the specific wear rate and coefficient of friction of AA7075 MMCs, leading to improved tribological performance characteristics. In addition, various wear mechanisms are briefly discussed in this study.