利用深度学习对深冲钢机械性能的预测

徐钢; 何金珊; 吕志民; 黎敏; 徐金梧

doi:10.1007/s12613-022-2547-8

Volume 30 Issue 1

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文章导航 > 矿物冶金与材料学报（英文） > 2023 > 30(1): 156-165

Gang Xu, Jinshan He, Zhimin Lü, Min Li, and Jinwu Xu, Prediction of mechanical properties for deep drawing steel by deep learning, Int. J. Miner. Metall. Mater., 30(2023), No. 1, pp. 156-165. https://doi.org/10.1007/s12613-022-2547-8

Cite this article as:

Gang Xu, Jinshan He, Zhimin Lü, Min Li, and Jinwu Xu, Prediction of mechanical properties for deep drawing steel by deep learning, Int. J. Miner. Metall. Mater., 30(2023), No. 1, pp. 156-165. https://doi.org/10.1007/s12613-022-2547-8

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研究论文

利用深度学习对深冲钢机械性能的预测

1)
北京科技大学钢铁共性技术协同创新中心，北京 100083，中国
2)
苏州宝联重工有限公司，苏州 215131，中国

通讯作者:
徐钢 E-mail: watermoon2012@gmail.com

文章亮点

(1) 采用机器学习方法建立数字孪生模型应用在IF钢在线力学性能预测。

(2) 采用GRU方法建立的数字孪生模型有更好的预测精度。

(3) 挖掘出的成分–工艺–性能映射关系可用于新材料研发和产品质量提升。

中文摘要

当前钢铁企业主要采用“事后抽检”方式来控制最终的产品质量。但因无法基于传统方法对所有产品实现在线质量预测，常发生索赔和退货，这也是导致企业经济损失的一大因素。在生产过程中为实现对深冲钢在线质量预测，引入了基于深度学习的机械性能预测模型。首先利用LSTM（长短时记忆）、GRU（门控循环单元）网络和GPR（高斯过程回归）模型去预测深冲钢的机械性能，并讨论了三种模式的预测精度和学习效率，其次提出了在线迁移学习模型。从结果来看不仅预测精度得到改善，而且预测耗时缩短能更好满足在线实时预测的要求。

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Research Article

Prediction of mechanical properties for deep drawing steel by deep learning

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Abstract

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.
- machine learning;
- recurrent natural network;
- transfer learning;
- on-line prediction;
- deep drawing steel;
- mechanical properties

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References

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