Indentation hardness

Composite number

Engineering

Materials science

Particle size

Computer science

Chemical engineering

Geology

Oceanography

Microstructure

Particle (ecology)

Composite material

Artificial neural network

Reinforcement

Machine learning

Science and Engineering of Composite Materials

In this study, an artificial neural network approach was employed to predict the effect of B 4 C size, B 4 C content, and milling time on the particle size and particle hardness of Al2024-B 4 C composite powders. Al2024-B 4 C powder mixtures with various reinforcement weight percentages (5%, 10%, and 20% B 4 C), 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-B 4 C composite powders produced with different reinforcement size, reinforcement ratio, and milling times.

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