Application of backpropagation neural network for spindle vibration-based tool wear monitoring in micro-milling

Abstract

This study develops a micro-tool condition monitoring system consisting of accelerometers on the spindle, a data acquisition and signal transformation module, and a backpropagation neural network. This study also discusses the effect of the sensor installations, selected features, and the bandwidth size of the features on the classification rate. To collect the vibration signals necessary for training the system model and verifying the system, an experiment was implemented on a micro-milling research platform along with a 700 μm diameter micro-end mill and a SK2 workpiece. A three-axis accelerometer was installed on a sensor plate attached to the spindle housing to collect vibration signals in three directions during cutting. The frequency domain features representing changes in tool wear were selected based on the class mean scatter criteria after transforming signals from the time domain to the frequency domain by fast Fourier transform. Using the appropriate vibration features, this study develops and tests a backpropagation neural network classifier. Results show that proper feature extraction for classification provides a better solution than applying all spectral features into the classifier. Selecting five features for classification provides a better classification rate than the case with four and three features along with the 30 Hz bandwidth size of the spectral feature. Moreover, combining the signals for tool condition from both direction signals provides a better classification rate than determining the tool condition using a one-direction single sensor

Online condition monitoring in micro-milling: A force waveform shape analysis approach

“…For the implementation of monitoring schemes, most of the current studies on TCM are based on empirical analysis [10], or sensing-oriented approaches such as motor current analysis [11], vibration analysis [12]- [15], and acoustic emission (AE) [8], [16], [17], or some of the combination approaches [18], [19]. In [10], Rahman et al found that the premature failure was the major factor that affected the micromilling tool life.…”

Compressive sensing and sparse decomposition in precision machining process monitoring: From theory to applications

“…The advantage of AE is that the signal measured is a source of engagement where the chip is formed, as were introduced in [10,11]. Vibration has also been one of the most widely studied signals for monitoring due to its convenient implementation; see for examples in [12,13]. Hsieh et al [13] detected tool wear with an accelerometer and showed that the vibration signals could be implemented in a neural network and used for micro-milling monitoring.…”

“…Vibration has also been one of the most widely studied signals for monitoring due to its convenient implementation; see for examples in [12,13]. Hsieh et al [13] detected tool wear with an accelerometer and showed that the vibration signals could be implemented in a neural network and used for micro-milling monitoring. Generally the vibration is brought by the cutting force variations, and as a result the vibration is less http://dx.doi.org/10.1016/j.mechatronics.2015.04.017 0957-4158/Ó 2015 Elsevier Ltd. All rights reserved.…”

Adaptive control optimization in micro-milling of hardened steels—evaluation of optimization approaches

“…As the critical aspect of the operation is the cutting-tool state (cutting-tool wear), sensors applied in tool condition monitoring are commonly used. A large number of research works have been conducted for cutting-tool monitoring and some of them applied to micromilling operations on hardened steels can be found in [12][13][14][15]. In general, the monitoring module is composed of a sensor system unit, amplification and conditioning unit, and a signal processing and feature extraction unit.…”

“…In general, the monitoring module is composed of a sensor system unit, amplification and conditioning unit, and a signal processing and feature extraction unit. In tool condition monitoring systems for machining operations, different sensors have been successfully applied, such as dynamometers [12,14,16,17], accelerometers [12,15], acoustic emission [13,14], etcetera. Different studies have been also conducted in signal processing and feature extraction [18][19][20][21].…”