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Prediction of mechanical properties for deep drawing steel by deep learning

International Journal of Minerals,Metallurgy and Materials 2023年第1期30卷 156-165页

作者： Gang Xu Jinshan He Zhimin Lü Min Li Jinwu Xu Collaborative Innovation Center of steel Technology University of Science and Technology BeijingBeijing 100083China Suzhou Baolian Heavy Industry Co. Suzhou 215131China

At present,iron and steel enterprises mainly use“after spot test ward”to control final product ***,it is impossible to realize on-line quality predetermining for all products by this traditional approach,hence claims and returns often occur,resulting in major eco-nomic losses of *** order to realize the on-line quality predetermining for steel products during manufacturing process,the predic-tion models of mechanical properties based on deep learning have been proposed in this ***,the mechanical properties of deep drawing steels were predicted by using LSTM(long short team memory),GRU(gated recurrent unit)network,and GPR(Gaussian process regression)model,and prediction accuracy and learning efficiency for different models were also ***,on-line re-learning methods for transfer learning models and model parameters were *** experimental results show that not only the prediction accuracy of optimized trans-fer learning models has been improved,but also predetermining time was shortened to meet real time requirements of on-line property *** industrial production data of interstitial-free(IF)steel was used to demonstrate that R2 value of GRU model in training stage reaches more than 0.99,and R2 value in testing stage is more than 0.96.

关键词： machine learning recurrent natural network transfer learning on-line prediction deep drawing steel mechanical properties

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