Prediction of cold rolling texture of steels using an Artificial Neural Network
Prediction of cold rolling texture of steels using an Artificial Neural Network
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DOI：
10.1016/j.commatsci.2009.04.014
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发表时间：
2009-10
影响因子：
3.3
通讯作者：
A. Brahme;M. Winning;D. Raabe
中科院分区：
文献类型：
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作者：
A. Brahme;M. Winning;D. Raabe
提出了一种基于人工神经网络的冷轧织构预测模型。这项工作的目标是设计一个能够在工程环境中快速在线预测纹理的模型。我们的方法使用一个前馈完全互连的神经网络与标准的反向传播误差算法配置连接器的权重。该模型使用纤维织构强度形式的织构数据以及碳含量、碳化物尺寸和轧制压下量作为模型的输入。模型的输出是纤维纹理数据的形式。将可用数据集分为训练集和测试集，以校准和测试网络。网络的预测提供了与训练集的边界框内的实验测量数据的极好匹配。
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.