Prediction of cold rolling texture of steels using an Artificial Neural Network

We present an Artificial Neural Network based model for the prediction of cold rolling textures of steels. The goal of this work was to design a model capable of fast online prediction of textures in an engineering environment. Our approach uses a feedforward fully interconnected neural network with standard backpropagation error algorithm for configuring the connector weights. The model uses texture data, in form of fiber texture intensities, as well as carbon content, carbide size and amount of rolling reduction as input to the model. The output of the model is in the form of fiber texture data. The available data sets are divided into training and test sets to calibrate and test the network. The predictions of the network provide an excellent match to the experimentally measured data within the bounding box of the training set.

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