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Improving imbalanced industrial datasets to enhance the accuracy of mechanical property prediction and process optimization for strip steel

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Abstract

The problem of imbalanced regression is widely prevalent in various intelligent manufacturing systems, significantly constraining the industrial application of machine learning models. Existing research has overlooked the impact of redundant data and has lost valuable information within unlabeled data, therefore, the effectiveness of the models is limited. To this end, we propose a novel model framework (sNN-ST, similarity-based nearest neighbor and Self-Training fusion) to address imbalanced regression in industrial big data. This approach comprises two main steps: first, we identify and remove redundant samples by analyzing the redundancy relationships among samples. Then, we perform pseudo-labeling on unlabeled data, selectively incorporating reliable and non-redundant samples into the labeled dataset. We validate the proposed method on two imbalanced regression datasets. Removing redundant data and effectively utilizing unlabeled data optimize the dataset's distribution and enhance its information entropy. Consequently, the processed dataset significantly improves the overall model performance. We used this model to conduct a Multi-Parameter Global Relative Sensitivity Analysis within a production system. This analysis optimized existing process parameters and improved product quality consistency. This research presents a promising approach to addressing imbalanced regression problems.

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Data availability

The data that support the findings of this study are openly available at https://gitee.com/superfeif/imbalanced-regression-datasets.git.

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Acknowledgements

This research supported by the National Natural Science Foundation of China (No.52004029), Fundamental Research Funds for the Central Universities (FRF-TT-20-06) and division 8, Xinjiang Production and Construction Corps Science and Technology Program (2022ZD02).

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Authors and Affiliations

  1. National Engineering Research Center of Flat Rolling Equipment, University of Science and Technology Beijing, Beijing, 100083, China

    Feifei Li, Anrui He, Yong Song, Chengzhe Shen, Fenjia Wang & Tieheng Yuan

  2. National Engineering Research Center for Advanced Rolling and Intelligent Manufacturing, University of Science and Technology Beijing, Beijing, 100083, China

    Shiwei Zhang, Xiaoqing Xu & Chao Liu

  3. China Academy of Machinery Science and Technology, No. 2 Shouti Nanlu, Haidian District, Beijing, 100044, China

    Yi Qiang

  4. Shihezi Zhonghe New Material Co., Ltd, Shihezi, 832000, China

    Pengfei Liu & Qiangguo Zhao

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  1. Feifei Li
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Li, F., He, A., Song, Y. et al. Improving imbalanced industrial datasets to enhance the accuracy of mechanical property prediction and process optimization for strip steel. J Intell Manuf (2023). https://doi.org/10.1007/s10845-023-02275-1

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