In the present article, artificial neural networks (ANNs) and genetic algorithm (GA) methodology were integrated to model tribological characteristics of stir-cast Al-Zn-Mg-Cu matrix composites under two-body abrasion considering large numbers of experimentally generated results. Tribo-responses of wear rate (Wrt), coefficient of friction (COF) and roughness of abraded surface (RAS) were evaluated under wide range of intrinsic ( i.e., particle quantity) and extrinsic ( i.e., abrasive size, load, distance and velocity) input parameters. Characteristics of Wrt, COF and RAS are often mutually contradictory in nature and so, multi-objective optimization technique becomes imperative for selection and design of machine components. Accordingly, those were optimized through Pareto solutions. Sensitivity of different factors was analyzed on each of the tribo-performances and validated via experimental evidences. Amongst the input variables, particle quantity and abrasive size dominated significantly over other variables except load which imparted modest influences. The role of various input parameters was explained through determination of different micromechanisms via exhaustive post wear characterizations, microstructural and surface topography attributes. Lowest values of Wrt and COF with a modest value of RAS were identified at 15 ± 2 wt.% particle quantity.
Multiscale modelling of hybrid glass fibres reinforced graphene platelets polyamide PA6 matrix composites for crashworthiness applications