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Online quantitative monitoring of milling cutter health condition based on deep convolutional autoencoder

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Abstract

The health condition of milling cutters (HCOMC) could heavily affect workpiece quality. However, it is extremely difficult to be quantified online. To solve this problem, an online quantitative monitoring method (OQM) is proposed based on a deep convolutional autoencoder (CAE). In this method, a health indicator (HI) is constructed for fast HCOMC monitoring. The OQM is composed of two parts, offline training and online monitoring. In the offline stage, the multi-sensor monitoring data that record in the cutter normal wear stage (named normal wear data, NWD) are selected from a subsampled life testing dataset to train a deep CAE. In the online stage, each monitoring data segment (MDS) is directly input into the trained CAE to obtain deep representations. Then, the HI is constructed by the mean square error (MSE) between the MDS and the deep representations to monitor the HCOMC. It is called convolutional-autoencoder-reconstruction-error-based health indicator (CARE-HI). In addition to the above-mentioned method, a new metric named isometric fusion metric (IFM) is also designed to assess HI. IFM is able to address the uneven problem of property contribution when using some widely used HI metrics. In the experiment, 28 milling cutters were subjected to cutting experiments under different working conditions. The experimental result demonstrates that the proposed OQM can efficiently improve feature quality and precisely monitor HCOMC. It also illustrates that the CARE-HI outperformed some existing ones in five metric dimensions. Therefore, the proposed CARE-HI can provide more accurate guidance for tool changing in machining.

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Funding

This research was supported in part by the National Science Fund for Distinguished Young Scholars under Grant 52105562, in part by the National Natural Science Foundation of China under Grant 51905452, in part by the Fundamental Research Funds for the Central Universities under Grant 2682022CX058, and in part by the Local Development Foundation guided by the Central Government under Grant 2020ZYD012.

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Authors and Affiliations

  1. School of Mechanical Engineering, Southwest Jiaotong University, Chengdu, 610031, Sichuan, China

    Yuncong Lei, Changgen Li, Liang Guo, Hongli Gao, Junhua Liang, Yi Sun & Jigang He

  2. Engineering Research Center of Advanced Drive Energy saving Technologies, Ministry of Education, Southwest Jiaotong University, Chengdu, 610031, Sichuan, China

    Yuncong Lei, Changgen Li, Liang Guo, Hongli Gao, Junhua Liang, Yi Sun & Jigang He

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  1. Yuncong Lei
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Contributions

Yuncong Lei and Changgen Li: writing—original draft, methodology, conceptualization, data curation, validation, investigation, and formal analysis. Liang Guo and Hongli Gao: writing—review and editing, funding acquisition, methodology, resources, and formal analysis. Junhua Liang: writing—review and editing, conceptualization, and formal analysis. Yi Sun: writing—review and editing, resources, and formal analysis. Jigang He: writing–review and editing, investigation, and formal analysis.

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Correspondence to Liang Guo.

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Lei, Y., Li, C., Guo, L. et al. Online quantitative monitoring of milling cutter health condition based on deep convolutional autoencoder. Int J Adv Manuf Technol 125, 4739–4752 (2023). https://doi.org/10.1007/s00170-023-10963-8

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  • DOI: https://doi.org/10.1007/s00170-023-10963-8

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