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An effective LSSVM-based approach for milling tool wear prediction

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Abstract

In order to realize real-time and precise monitoring of the tool wear in the milling process, this paper presents a tool wear predictive model based on the stacked multilayer denoising autoencoders (SMDAE) technique, the particle swarm optimization with an adaptive learning strategy (PSO-ALS), and the least squares support vector machine (LSSVM). Cutting force and vibration information are adopted as the monitoring signals. Three steps make up the unique feature extraction and fusion method: multi-domain features extraction, principal component analysis (PCA)-based dimension reduction, and SMDAE-based dimension increment. As a novel feature representation learning approach, the SMDAE technique is utilized to fuse the PCA-based fusion features to enrich the effective information by increasing the dimension, thus helping polish up the predictive performance of the proposed model. PSO-ALS is used to obtain the optimal parameters for LSSVM, simplifying the problem and increasing the population diversity. Twelve sets of milling experiments are conducted to demonstrate the reliable performance of the proposed model. The experimental results show that the presented model is superior to models such as PSO-LSSVM in predictive performance, and the SMDAE technique effectively improves the prediction accuracy of the established model. The findings of this paper offer theoretical guidelines for monitoring milling tool wear in real industrial situations.

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Funding

This research was supported by the Key R & D project of Shandong Province (No. 2019JZZY010445).

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Authors and Affiliations

  1. Key Laboratory of High Efficiency and Clean Mechanical Manufacture, Ministry of Education of China, School of Mechanical Engineering, Shandong University, Jinan, China

    Yingshang Ge, Jianhua Zhang, Guohao Song & Kangyi Zhu

  2. National Demonstration Center for Experimental Mechanical Engineering Education, School of Mechanical Engineering, Shandong University, Jinan, China

    Yingshang Ge, Jianhua Zhang, Guohao Song & Kangyi Zhu

Authors
  1. Yingshang Ge
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  2. Jianhua Zhang
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  3. Guohao Song
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  4. Kangyi Zhu
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Contributions

The research progress is supported by contributions from all authors. Experimental design, data acquisition, and algorithmic construction were implemented by Yingshang Ge, Jianhua Zhang, Guohao Song, and Kangyi Zhu. The first draft was written by Yingshang Ge. All authors commented on previous versions of the manuscript. The final draft read and approved by all authors.

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Correspondence to Jianhua Zhang.

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Ge, Y., Zhang, J., Song, G. et al. An effective LSSVM-based approach for milling tool wear prediction. Int J Adv Manuf Technol 126, 4555–4571 (2023). https://doi.org/10.1007/s00170-023-11421-1

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