Abstract
A neural network (ANN) model was developed to predict the abrasive wear behavior of AA2024 aluminum alloy matrix composites reinforced with B4C particles. Al2024-B4C powder mixtures with various reinforcement volume fractions (3-10%) and particle sizes (29µm and 71 µm) were prepared and Al2024-B4C composites were produced by stir-casting technique. The model was based on three layer neural network with feed forward back propagation learning algorithm. A sigmoid transfer function was developed and found to be suitable for analyzing the abrasive wear behavior of composites with the least error. The training data are collected by the experimental setup in the laboratory. The trained model was used to study the effect of ceramic particle size and volume fraction on the abrasive wear of Al2024-B4C composites. By comparing the predicted values with the experimental data, it was demonstrated that the well-trained feed forward back propagation ANN model is a powerful tool for prediction of abrasive wear behavior of Al2024-B4C composites.
Highlights
Aluminum-matrix composites (AMCs) reinforced with particles and whiskers are widely used for high performance applications such as in automotive, military, aerospace and electricity industries because of their improved physical and mechanical properties [1]
The particle size of B4C reinforcement had a considerable effect on the abrasion wear resistance of the composites, which is increased with increasing particle size
The use of Artificial Neural Network (ANN) to study the effect of the sliding time, volume fraction and ceramic particle size on the volume loss, specific wear rate and surface roughness of Al2024/B4C composites was explored in this study
Summary
Aluminum-matrix composites (AMCs) reinforced with particles and whiskers are widely used for high performance applications such as in automotive, military, aerospace and electricity industries because of their improved physical and mechanical properties [1]. The ceramic particle-reinforced composites are being produced by different methods, such as stir casting [8], powder metallurgy [9]. Among these methods, stir casting is considered to be adaptable and economically viable due to its low processing cost and high production rate. An additional benefit of this process is the near-net shape formation of the composites [10]
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