Prediction of wear behavior of aluminum alloy reinforced with carbon nanotubes using nonlinear identification

Abstract

Aluminum metal matrix composites reinforced with particulates have attracted much attention in the automotive industry, due to their improved wear resistance in comparison to aluminum alloys, in recent years. The wear behavior is the critical factor influencing the product life and performance in engineering components. Carbon nanotubes (CNT) are one of the most promising candidates of reinforcements used to improve mechanical strength such as wear in metal matrix composites (MMCs). However, in industrial applications, wear tests are relatively expensive and prolonged. As a result, for several years, research has been increasingly concentrated on development of wear prediction models. In this study, prediction of wear behavior of aluminum (Al) matrix (MMCs) reinforced with different amounts (0, 0.5, 1 and 2 wt%) of CNTs was investigated. A nonlinear autoregressive exogenous (NARX) model structure was chosen for the modeling. The wear load was considered as the input parameter, whereas the wear rate and friction coefficient as the output parameters. Simulations using the identified models were compared with experimental results and it was found that the modeling of wear process was satisfactory. Copyright © 2014 SAE International.