Abstract
Prediction the mechanical properties is very important in many real-life industry fields. In this paper, we proposed an efficient convolutional neural network (CNN) to predict the mechanical properties of hot roll steel. In this study, 20,000 sets of data are collected from the hot roll factory, where 16,000 sets of data were used for training the CNN model, and 4,000 sets of data were used for testing the performance of the model. Compared with Support Vector Machine (SVM) and Artificial Neural Network (ANN), The experimental results have been demonstrated to provide a competitive and higher prediction accuracy.
Supported by the National Natural Science Foundation of China (Grant Nos. U1803262, 61702383, 61602350).
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Xu, H., Xu, Z., Zhang, K. (2020). Mechanical Properties Prediction for Hot Roll Steel Using Convolutional Neural Network. In: Pan, L., Liang, J., Qu, B. (eds) Bio-inspired Computing: Theories and Applications. BIC-TA 2019. Communications in Computer and Information Science, vol 1160. Springer, Singapore. https://doi.org/10.1007/978-981-15-3415-7_47
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DOI: https://doi.org/10.1007/978-981-15-3415-7_47
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