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Assembly quality evaluation for linear axis of machine tool using data-driven modeling approach

Author

Listed:
  • Yang Hui

    (Xi’an Jiaotong University
    Xi’an Jiaotong University
    Xi’an Jiaotong University)

  • Xuesong Mei

    (Xi’an Jiaotong University
    Xi’an Jiaotong University
    Xi’an Jiaotong University)

  • Gedong Jiang

    (Xi’an Jiaotong University
    Xi’an Jiaotong University
    Xi’an Jiaotong University)

  • Fei Zhao

    (Xi’an Jiaotong University
    Xi’an Jiaotong University
    Xi’an Jiaotong University)

  • Ziwei Ma

    (Xi’an Jiaotong University)

  • Tao Tao

    (Xi’an Jiaotong University
    Xi’an Jiaotong University
    Xi’an Jiaotong University)

Abstract

During the batch assembly analysis of linear axis of machine tool, assembly quality evaluation is crucial to reduce assembly quality fluctuations and improve efficiency. This study presented a data-driven modeling approach for evaluating assembly quality of linear axis based on normalized mutual information and random sampling with replacement (NMI-RSWR) variable selection method, synthetic minority over-sampling technique (SMOTE), and genetic algorithm (GA)-optimized multi-class support vector machine (SVM). First, a variable selection method named NMI-RSWR was proposed to select key assembly parameters which affected assembly quality of linear axis. Then, a hybrid method based on SMOTE and GA-optimized multi-class SVM was presented to construct assembly quality evaluation model. In this method, Class imbalance problem was solved by using SMOTE, and parameters optimization problem was solved by using GA. Finally, the assembly-related data from the batch assembly of x-axis of a three-axis vertical machining center were collected to validate the proposed method. The results indicate that the proposed NMI-RSWR approach has capacity for selecting the highly related assembly parameters with assembly quality of linear axis, and the proposed data-driven modeling approach is effective for assembly quality evaluation of linear axis.

Suggested Citation

  • Yang Hui & Xuesong Mei & Gedong Jiang & Fei Zhao & Ziwei Ma & Tao Tao, 2022. "Assembly quality evaluation for linear axis of machine tool using data-driven modeling approach," Journal of Intelligent Manufacturing, Springer, vol. 33(3), pages 753-769, March.
    • Handle: RePEc:spr:joinma:v:33:y:2022:i:3:d:10.1007_s10845-020-01666-y
    DOI: 10.1007/s10845-020-01666-y
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    References listed on IDEAS

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    1. Zhe Wei & Yixiong Feng & Zhaoxi Hong & Rongxia Qu & Jianrong Tan, 2017. "Product quality improvement method in manufacturing process based on kernel optimisation algorithm," International Journal of Production Research, Taylor & Francis Journals, vol. 55(19), pages 5597-5608, October.
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    4. Sheng Hu & Liping Zhao & Yiyong Yao & Rushan Dou, 2016. "A variance change point estimation method based on intelligent ensemble model for quality fluctuation analysis," International Journal of Production Research, Taylor & Francis Journals, vol. 54(19), pages 5783-5797, October.
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