Zhengzhong Wang
ABSTRACT
A multi-channel parallel convolutional neural network model is proposed to predict the mechanical properties of hot-rolled steel by chemical content and process parameters. The innovative contribution of this paper is to propose an improved Gramian Angular field method in the field of steel performance prediction, which converts the original data into a two-dimensional image matrix and introduces new features. Compared with the traditional convolutional neural network model, the multi-channel parallel convolutional neural network uses private blocks to decouple data and public blocks to integrate key data, which effectively improves the prediction accuracy of the model. Experiments in this paper show that the optimal hyperparameter structure model proposed in this paper has higher prediction accuracy than similar models proposed in other literatures.
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Index Terms
- Prediction of mechanical properties of hot-rolled steel based on multi-channel convolutional neural network
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