Wear resistance studies for the multilayer laminate nanocomposite experimentally need a lot of time and money to investigate the effects of different parameters over it. Furthermore, the impact of these parameters on the surface occurs within a microsecond making the investigation very difficult. Statistical methods such as response surface methodology can be an effective technique in this matter. In the current work, the response surface methodology and the artificial neural network were used to design and analyze the weight loss at different temperatures, sliding speeds, applied loads, and weight percentages of graphene nanoplatelet (GNP) for multilayers laminate composite consists of polyurethane resin and anhydride hardener reinforced with 30 volume fraction of random fiber-glass. It was found that increasing GNP content could effectively increase friction coefficient and reduce the wear rate of multilayer laminate nanocomposite. The predicted values from artificial neural networks and experimental methods are found very close to each other.

Keywords: nanocomposites, wear resistance, response surface methodology, artificial neural network.