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Prediction of mechanical properties for deep drawing steel by deep learning

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Abstract

At present, iron and steel enterprises mainly use “after spot test ward” to control final product quality. However, 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 economic losses of enterprises. In order to realize the on-line quality predetermining for steel products during manufacturing process, the prediction models of mechanical properties based on deep learning have been proposed in this work. First, 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 discussed. Then, on-line re-learning methods for transfer learning models and model parameters were proposed. The experimental results show that not only the prediction accuracy of optimized transfer learning models has been improved, but also predetermining time was shortened to meet real time requirements of on-line property predetermining. The 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.

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Acknowledgements

This work was financially supported by the National Natural Science Foundation of China (No. 52175284) and the State Key Lab of Advanced Metals and Materials in University of Science and Technology Bei**g (No. 2021-ZD08).

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Authors and Affiliations

  1. Collaborative Innovation Center of Steel Technology, University of Science and Technology Bei**g, Bei**g, 100083, China

    Gang Xu, **shan He, Zhimin Lü, Min Li & **wu Xu

  2. Suzhou Baolian Heavy Industry Co., Suzhou, 215131, China

    Gang Xu

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  1. Gang Xu
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  2. **shan He
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  4. Min Li
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Correspondence to Gang Xu.

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Xu, G., He, J., Lü, Z. et al. Prediction of mechanical properties for deep drawing steel by deep learning. Int J Miner Metall Mater 30, 156–165 (2023). https://doi.org/10.1007/s12613-022-2547-8

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  • DOI: https://doi.org/10.1007/s12613-022-2547-8

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