Investigation of Manufacturing Parameters on the Mechanical Properties of Powder Metallurgy Magnesium Matrix Nanocomposite by Artificial Neural Networks

摘要

In present study, Artificial Neural Network (ANN) approach to prediction of the ODS Magnesium matrixudcomposite mechanical properties obtained was used. Several composition of Mg- Al2O3 composites withudfour different amount of Al2O3 reinforcement with four different size of nanometer to micrometer were producedudin different sintering times. The specimens were characterized using metallographic observation,udmicrohardness and strength (UTS) measurements. Then, for modeling and prediction of mentioned conditions,uda multi layer perceptron back propagation feed forward neural network was constructed to evaluateudand compare the experimental calculated data to predicted values. In neural network training modules,uddifferent composition, sintering time and reinforcement size were used as input (3 inputs), hardness andudUltimate Tensile Strength(UTS) were used as output. Then, the neural network was trained using theudprepared training set. At the end of training process the test data were used to check the system’s accuracy.udAs a result, the comparison of neural network output results with the results from experiments andudempirical relationship has shown good agreement with average error of 2.5%.When you are citing the document, use the following link http://essuir.sumdu.edu.ua/handle/123456789/35119