Hybrid nanocomposites of aluminium (NHAMMCs) made from AA5052 are fabricated via stir casting route by reinforcing 12 wt% Si3N4 and 0.5 wt% of graphene for usage in aeronautical and automotive applications due to the lower density and higher strength to weight proportion. The wear characteristics of the NHAMMCs are evaluated for different axial load, rotational speed, sliding distance and sliding time based on Box-Behnken design (BBD) of response surface methodology (RSM). Orowan strengthening mechanism is identified from optical image which improves the strength of the composite. Outcomes show that with higher axial load and rotational speed, there is substantial increase in wear loss whereas with increased sliding distance and sliding time there is no considerable increase in wear loss due to the lubricating nature of the reinforced graphene particles since it has higher surface area to volume ratio. Besides, artificial intelligence approach of neuro-fuzzy (ANFIS) model is developed to predict the output responses and the results are compared with the regression model predictions. Prediction from ANFIS outplays the regression model prediction.

Predicting the wear performance of graphene and silicon nitride reinforced aluminium hybrid nanocomposites using artificial intelligence approach
    Abstract
    1 Introduction
    2 Materials and methods
        2.1 Ultrasonic cavitation aided stir casting
        2.2 Pin-on-disc
        2.3 Response surface methodology
        2.4 Adaptive neuro-fuzzy inference system (ANFIS)
    3 Results and discussion
        3.1 Analysis and modelling of wear loss
        3.2 Analysis and modelling of friction force
        3.3 Analysis and modelling of CoF
        3.4 Desirability analysis
        3.5 ANFIS model for prediction
    4 Conclusion
    Acknowledgement 
    References

• Praveen Raj(Department of Mechanical Engineering, Jyothi Engineering College, Thrissur, Kerala 679531, India, APJ Abdul Kalam Technological University, Thiruvananthapuram 695016, India)
• P. L. Biju(Department of Mechanical Engineering, Jyothi Engineering College, Thrissur, Kerala 679531, India)
• B. Deepanraj(Department of Mechanical Engineering, Jyothi Engineering College, Thrissur, Kerala 679531, India)
• N. Senthilkumar(Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Chennai, Tamil Nadu 602105, India)