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Tool wear condition monitoring method based on relevance vector machine

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Abstract

During the machining process, the tool wear state is closely related to the quality of the workpiece, which will directly affect the performance of the equipment. Not timely replacement of tools will lead to increased processing costs, low workpiece surface quality, and even damage to processing equipment. Therefore, research on tool wear monitoring is necessary for the tool processing industry. By analyzing the relationship between tool wear and sensor signals to determine the required acquisition signal. Aiming at the problem that the original sensor data cannot be directly used in the machining process, the signal processing technology is used to preprocess the original signal, remove the invalid signal collected during the cutting process, and use the filtering method to eliminate the singular points in the original signal. The time domain and frequency domain features of the data are extracted. Firstly, the features are optimized by the extreme random tree (ET), and the tool wear is taken as the target vector. The Pearson correlation coefficient (PCC) between the target vector and the filtered features is calculated, and the features with solid correlation with the target vector are selected. The results show that the relevance vector machine (RVM) model proposed in the research can effectively monitor tool wear.

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Data availability

The datasets used or analyzed during the current study are available from the corresponding author on reasonable request.

Code availability

Not applicable.

Abbreviations

ET:

Extreme random tree

PCC:

Pearson correlation coefficient

RVM:

Relevance vector machine

Di :

Data set

K :

Feature number

S :

Characteristic value

p i :

Frequency of feature occurrence

Gini(k) :

Gini coefficient

ρ :

Population correlation coefficient

r :

Pearson correlation coefficient value

X i :

Sample point

Y i :

Sample point

A :

Diagonal matrix

Φ :

Kernel matrix

\(\Gamma ()\) :

Gamma function

μ :

Posterior mean value

\(\overline{X }\) :

Sample mean

\({\sigma }_{X}\) :

Sample standard deviation

\({t}_{n}\) :

Target sample data

R :

Sample data vector value

\({R}_{d}\) :

Output target value

y :

Objective function

\({\xi }_{n}\) :

Additional Gaussian noise

\({\sigma }_{v}^{2}\) :

Variance

\(K(x,{x}_{i})\) :

Kernel function

\({\sigma }^{2}\) :

Noise parameter

t :

Objective vector

N :

Kernel function matrix dimension

u :

Parameter vector

\(\alpha\) :

Hyper parameter

\(\omega\) :

Weight

\(\Sigma\) :

Covariance

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Funding

This research is supported by the National Natural Science Foundation of China (Grant Number 52175393), supported by Research Team Project of Heilongjiang Natural Science Foundation(TD2022E003).

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Authors and Affiliations

  1. Key Laboratory of Advanced Manufacturing and Intelligent Technology, Ministry of Education, Harbin University of Science and Technology, Harbin, 150080, China

    Ruhong Jia, Caixu Yue, Qiang Liu, Wei Xia, Yiyuan Qin & Mingwei Zhao

Authors
  1. Ruhong Jia
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  2. Caixu Yue
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  3. Qiang Liu
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  4. Wei Xia
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  5. Yiyuan Qin
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  6. Mingwei Zhao
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Contributions

Ruhong Jia has organized the project, analyzed and arranged data, and wrote the manuscript; Caixu Yue, Qiang Liu, and Wei Xia analyzed data; Yiyuan Qin and Mingwei Zhao helped perform the analysis with constructive discussions.

Corresponding author

Correspondence to Caixu Yue.

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The content studied in this article belongs to the field of metal processing and does not involve humans and animals. This article strictly follows the accepted principles of ethical and professional conduct.

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Jia, R., Yue, C., Liu, Q. et al. Tool wear condition monitoring method based on relevance vector machine. Int J Adv Manuf Technol 128, 4721–4734 (2023). https://doi.org/10.1007/s00170-023-12237-9

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