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Prediction of machine tool spindle assembly quality variation based on the stacking ensemble model

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Abstract

This paper addresses the challenges of traditional spindle assembly methods, which rely on trial-and-error approaches, hindering new product development evaluation. A stacking ensemble model is proposed to predict the assembly quality variation of machine tool spindles. The model uses data from 925 single-spindle inspections and extracts evaluation metrics from multiple domains to extract valuable information. Feature selection is performed using a correlation model to identify important features, and various lightweight supervised learning algorithms are applied to analyze the data. To further enhance the model’s performance, a stacking ensemble approach is proposed, which combines algorithms. The results demonstrate that the proposed stacking ensemble model is an effective approach for predicting the assembly quality variation of machine tool spindles, using the data available. The proposed ensemble model enhances quality control processes in spindle assembly, enabling practitioners to identify key features and predict machine tool spindle assembly quality variations more accurately.

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Funding

This work was supported in part by the Advanced Institute of Manufacturing with High-tech Innovations (AIM-HI) from The Featured Areas Research Center Program within the framework of the Higher Education Sprout Project by the Ministry of Education (MOE) in Taiwan and was also supported in part by the Ministry of Science and Technology, Taiwan, ROC, under Grant NSTC 112–2218-E-194–007.

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Authors and Affiliations

  1. Yeong Chin Machinery Industries Co. Ltd, Taichung, 429, Taiwan

    Min-Sin Liu

  2. Department of Mechanical Engineering, National Chung Cheng University, Chiayi, 621, Taiwan

    Min-Sin Liu

  3. Department of Mechanical Engineering and Advanced Institute of Manufacturing With High-Tech Innovations, National Chung Cheng University, Chiayi, 621, Taiwan

    Ping-Huan Kuo & Shyh-Leh Chen

Authors
  1. Min-Sin Liu
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  2. Ping-Huan Kuo
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  3. Shyh-Leh Chen
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Contributions

ML developed the theory and carried out the simulations and experiments under the supervision of SC and PK. Three authors participated in the analysis and interpretation of the data. The manuscript was originally drafted by ML, and reviewed and edited by SC and PK. All three authors read and approved the final manuscript.

Corresponding author

Correspondence to Shyh-Leh Chen.

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Liu, MS., Kuo, PH. & Chen, SL. Prediction of machine tool spindle assembly quality variation based on the stacking ensemble model. Int J Adv Manuf Technol (2024). https://doi.org/10.1007/s00170-024-13766-7

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