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Imputation Method Based on Collaborative Filtering and Clustering for the Missing Data of the Squeeze Casting Process Parameters

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Abstract

The development of a highly efficient methodology for establishing squeeze casting process parameters from past data is essential. However, designing squeeze casting process parameters based on past data is difficult when there are many missing values. Conventional missing data approaches are fraught with additional computational challenges when applied to high-dimensional multivariable missing data, especially material process data with correlation. As the relationship between material composition and process parameters has similar characteristics with that between users and information of interest, this paper proposes a method for missing data imputation based on a clustering-based collaborative filtering (ClubCF) algorithm to address this challenge. Data samples with and without missing values were divided into two groups. K-means clustering based on a canopy algorithm was applied to the data samples without missing values to obtain k subclass data, whose values were then selected to fill data samples with missing values via a collaborative filtering theory based on Pearson similarity user filling. The missing squeeze casting process parameters data of aluminum alloys were used to evaluate the method, and more comparative experiments were carried out to understand their performance and features. Two different indicators, including the mean absolute error and the standard deviation, were utilized to quantify the imputation performance, which was compared with those of three conventional methods (mean interpolation, regression interpolation, and the expectation maximization algorithm). The results indicate that the proposed approach is effective and outperforms conventional methods in processing high-dimensional correlated data.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (Grant Nos. 51965006, 51875209), Guangxi Natural Science Foundation (Grant No. 2018GXNSFAA050111), and the Open Fund of the National Engineering Research Center of Near-Net-Shape Forming for Metallic Materials (Grant No. 2019001).

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Authors and Affiliations

  1. Guangxi Key Lab of Manufacturing System and Advanced Manufacturing Technology, Guangxi University, Nanning, 530003, China

    Jianxin Deng, Zhixing Ye & Lubao Shan

  2. School of Mechanical Engineering, Guangxi University, Nanning, 530003, China

    Jianxin Deng & Guangming Liu

  3. National Engineering Research Center of Near-Net-Shape Forming for Metallic Materials, South China University of Technology, Guangzhou, 510640, China

    Dongdong You

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  1. Jianxin Deng
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Deng, J., Ye, Z., Shan, L. et al. Imputation Method Based on Collaborative Filtering and Clustering for the Missing Data of the Squeeze Casting Process Parameters. Integr Mater Manuf Innov 11, 95–108 (2022). https://doi.org/10.1007/s40192-021-00248-x

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