New Tool Wear Estimation Method of the Milling Process Based on Multisensor Blind Source Separation

Chen Gao,Sun Bintao,Mengjuan Peng,Yuqing Zhou,Heng Wu,Akemi Gálvez

doi:10.1155/2021/9985870

Abstract

Timely and effective identification and monitoring of tool wear is important for the milling process. However, traditional methods of tool wear estimation have run into difficulties due to under small samples with less prior knowledge. This article addresses this issue by employing a multisensor tool wear estimation method based on blind source separation technology. Stationary subspace analysis (SSA) technology is applied to transform multisensor signals to stationary and nonstationary sources without prior information of signals. Ten dimensionless time-frequency indices of the nonstationary signal are extracted to train least squares support vector regression (LS-SVR) to obtain a tool wear estimation model for small samples. The analysis and comparison of one benchmark tool wear dataset and tool wear experiments verify the feasibility and effectiveness of the proposed method and outperform other two current methods.

Highlights

(1) A tool wear estimation method for a milling process based on a multisensor blind source separation method is proposed, using small training sample sizes and not presetting model parameters (2) e proposed method based on subspace analysis (SSA) and least squares support vector regression (LS-SVR) significantly outperforms principal component analysis (PCA) according to milling tool wear experiments (3) Experiments with different cutting conditions verify that the proposed method is robust and promising for milling tool condition monitoring e remainder of this study is organized as follows
3-flute ball nose 10400 rpm mm/min 0.125 mm 0.2 mm 5 7 50 kHz were calculated as the input of the LS-SVR. ere are two reasons for selecting one nonstationary source: (1) it contains a variety of feature information than the stationary source to distinguish different tool wear; and (2) in the proposed method, it is easy to calculate the statistical parameters for the single nonstationary source
It can be seen that the time sequence diagram of the nonstationary source changes significantly after SSA transformation, while the changes of eight stationary sources after SSA transformation are not obvious. erefore, we only used the signal of the nonstationary source to estimate tool wear

Summary

Introduction

(1) A tool wear estimation method for a milling process based on a multisensor blind source separation method is proposed, using small training sample sizes and not presetting model parameters (2) e proposed method based on SSA and LS-SVR significantly outperforms PCA according to milling tool wear experiments (3) Experiments with different cutting conditions verify that the proposed method is robust and promising for milling tool condition monitoring e remainder of this study is organized as follows.

Methods

Results

Conclusion