Tool Wear Monitoring with Vibration Signals Based on Short-Time Fourier Transform and Deep Convolutional Neural Network in Milling

Zhiwen Huang,Xiaoru Li,Jianmin Zhu,Jingtao Lei,Fengqing Tian,Xiaoliang Jin

doi:10.1155/2021/9976939

Zhiwen Huang, Xiaoru Li + Show 4 more

Open Access

https://doi.org/10.1155/2021/9976939

Copy DOI

Abstract

Tool wear monitoring is essential in precision manufacturing to improve surface quality, increase machining efficiency, and reduce manufacturing cost. Although tool wear can be reflected by measurable signals in automatic machining operations, with the increase of collected data, features are manually extracted and optimized, which lowers monitoring efficiency and increases prediction error. For addressing the aforementioned problems, this paper proposes a tool wear monitoring method using vibration signal based on short-time Fourier transform (STFT) and deep convolutional neural network (DCNN) in milling operations. First, the image representation of acquired vibration signals is obtained based on STFT, and then the DCNN model is designed to establish the relationship between obtained time-frequency maps and tool wear, which performs adaptive feature extraction and automatic tool wear prediction. Moreover, this method is demonstrated by employing three tool wear experimental datasets collected from three-flute ball nose tungsten carbide cutter of a high-speed CNC machine under dry milling. Finally, the experimental results prove that the proposed method is more accurate and relatively reliable than other compared methods.

Highlights

In the metal cutting process, tool wear seriously reduces the workpiece quality, lowers the machining efficiency, and increases the manufacturing costs; tool wear monitoring becomes increasingly significant in precision machining [1]
For quantitatively evaluating the effectiveness of the tool wear monitoring method proposed in this paper, the following two measures are used, that is, Mean Absolut Error (MAE) and Root Mean Squared Error (RMSE)
We propose a tool wear monitoring method based on short-time Fourier transform (STFT) and DNNN in milling operations, which utilizes the time-frequency maps based on STFT as image representation of vibration signals and establishes the nonlinear relationship between obtained images and tool flank wear width based on the constructed deep convolutional neural network (DCNN) model

Summary

Introduction

In the metal cutting process, tool wear seriously reduces the workpiece quality, lowers the machining efficiency, and increases the manufacturing costs; tool wear monitoring becomes increasingly significant in precision machining [1]. Wang et al adopted dimension reduction methods to select 54 features extracted from three directional force and vibration signals and applied support vector regression (SVR) to predict tool wear [4]. Kong et al used the integrated radial basis function-based kernel principal component analysis (KPCA) to fuse 48 features extracted from the three orthogonal cutting forces and constructed the Gaussian process regression-based tool wear predictive model [6]. Yu et al selected root mean square of the vibration signal and developed weighted hidden Markov model for tool wear monitoring [7]. Ghosh et al employed feature space filtering to find the most informative features extracted from sensors and developed a neural network-based fusion model for tool wear monitoring [9]. Wu et al used correlation analysis, monotonicity, and autocorrelation to optimize 66 features extracted from multisensor signals, fused features through

Methods

Results

Conclusion