Prediction of density, porosity and hardness in aluminum–copper-based composite materials using artificial neural network

Abstract

The potential of using feed forward backpropagation neural network in prediction of some physical properties and hardness of aluminium–copper/silicon carbide composites synthesized by compocasting method has been studied in the present work. Two input vectors were used in the construction of proposed network; namely weight percentage of the copper and volume fraction of the reinforced particles. Density, porosity and hardness were the three outputs developed from the proposed network. Effects of addition of copper as alloying element and silicon carbide as reinforcement particles to Al–4 wt.% Mg metal matrix have been investigated by using artificial neural networks. The maximum absolute relative error for predicted values does not exceed 5.99%. Therefore, by using ANN outputs, satisfactory results can be estimated rather than measured and hence reduce testing time and cost.

Introduction

The term “composite” broadly refers to a material system which is composed of a discrete constituents (the reinforcements) distributed in a continuous phase (the matrix) which derives its distinguishing characteristics from the properties of its constituents, from the geometry and architecture of the constituents, and from the properties of the boundaries (interfaces) between different constituents. Composite materials are usually classified on the basis of the physical or chemical nature of the matrix phase, e.g., polymer matrix, metal–matrix and ceramic matrix composites.

Aluminum matrix composites (AMCs) refer to the class of light-weight high performance aluminum centric material systems. The reinforcement in AMCs could be in the form of continuous/discontinuous fibers, whisker or particulates, in volume fractions ranging from a few percent to 70% (Surappa, 2003). Candan and Bilgic (2004) produced Al–60 vol.%SiC_P composites by pressure infiltration technique and investigated their corrosion behavior, in general they found that Al-based composites are weaker in 3.5% NaCl solution compared to Al–4 wt.%Mg alloy Al-based composites are usually reinforced by Al₂O₃, SiC, and C. In addition SiO₂, B, BN, B₄C may also be considered. Properties of AMCs can be tailored to the demands of different industrial applications by suitable combinations of matrix, reinforcement and processing route. In the last few years, AMCs have been utilized in high-tech structural and functional applications including aerospace, defense, automotive, and thermal management areas, as well as in sports and recreation.

The major advantages of AMCs compared to unreinforced materials are mentioned in most of articles dealt with aluminium-based composite materials (e.g. Szezepanik and Sleboda, 1996, Smith and Hashemi, 2006, Chaudhurky et al., 2004) which can be summarized as follows: greater strength, improved stiffness, reduced density, good corrosion resistance, improved high temperature properties, controlled thermal expansion coefficient, thermal/heat management, enhanced and tailored electrical performance, improved wear resistance and improved damping capabilities.

Among the various methods to fabricate metal matrix composites, stir casting method has drawn keen attraction among the researchers due to its industrial feasibility. The major limitation in many cases in fabricating metal matrix composites by liquid phase route resides upon the incompatibility of the reinforcement and the matrix (Candan and Bilgic, 2004, Hassan et al., 2007). This problem in case of Al-based metal matrix composite is due to the formation of stable tenacious oxide film, resulting in poor wettability with the ceramic particle. One of the common practices to improve wettability of an Al melt is through addition of small amount of reactive metals like magnesium and titanium prior to the incorporation of ceramic particle. In the present work, 4 wt.%Mg was added to Al to improve wettability as recommended by Candan and Bilgic (2004); Hassan et al. (2007) and argon flux was used during melting and pouring tasks to reduce oxidation effect.

The use of artificial neural networks (ANNs) represents a new methodology in many different applications of composite materials including prediction of mechanical properties (e.g. Altinkok and Koker, 2006, Durmus et al., 2006, Lee et al., 1999, Mukherjee et al., 1995, Zhang et al., 2002, Zhang et al., 2003). It is a promising field of research in predicting experimental trends and has become increasingly popular in the last few years as they can often solve problems much faster compared to other approaches with the additional ability to learn from small experimental data. ANN was used to predict wear loss and surface roughness of AA 6351 aluminum alloy by Durmus et al. (2006). In their study, experimental and ANNs results have been compared and they showed coincidence to a large extent. The use of ANN for prediction of physical properties and tensile strengths in particle reinforced aluminum matrix composites showed satisfactory results if ANN used as prediction technique (Altinkok and Koker, 2005, Altinkok and Koker, 2006). Vassilopoulos et al. (2007) used ANN in spectrum fatigue life prediction of composite materials. In their study they found that the main benefit of this tool is that only a small portion, in the range of 40–50%, of the experimental data is needed for the whole analysis. Thus, expensive and time consuming tests required by the conventional way for the establishment of S–N curves could be significantly reduced without significant loss of accuracy.