Prediction of the influence of processing parameters on synthesis of Al2024-B4C composite powders in a planetary mill using an artificial neural network

• Main Authors: Varol Temel, Canakci Aykut, Ozsahin Sukru
• Format: Article
• Language: English
• Published: De Gruyter 2014-06-01
• Series: Science and Engineering of Composite Materials
• Subjects: artificial neural networks; mechanical alloying; metal-matrix composites (mmcs); powder processing
• Online Access: https://doi.org/10.1515/secm-2013-0148
• ISSN: 0792-1233; 2191-0359

In this study, an artificial neural network approach was employed to predict the effect of B4C size, B4C content, and milling time on the particle size and particle hardness of Al2024-B4C composite powders. Al2024-B4C powder mixtures with various reinforcement weight percentages (5%, 10%, and 20% B4C), reinforcement size (49 and 5 μm), and milling times (0–10 h) were prepared by mechanical alloying process. The properties of the composite powders were analyzed using a laser particle size analyzer for the particle size and a microhardness tester for the powder microhardness. The three input parameters in the proposed artificial neural network (ANN) were the reinforcement size, reinforcement ratio, and milling time. Particle size and particle hardness of the composite powders were the outputs obtained from the proposed ANN. The mean absolute percentage error for the predicted values did not exceed 4.289% for the best prediction model. This model can be used for predicting properties of Al2024-B4C composite powders produced with different reinforcement size, reinforcement ratio, and milling times.