1. Introduction
Compared with the traditional manufacturing industry, the advanced manufacturing industry is moving towards intelligent development; however, there are many problems to be solved in the development of modern manufacturing. One problem is whether the tool status can be accurately monitored in real time. Tool wear is a common problem in precision manufacturing, which affects the productivity of modern high-speed computer numerical control (CNC) manufacturing greatly [
1]. Severe tool wear may lead to not only scrapped components but, also, possible damage to the machine tool; therefore, it is necessary to establish a reliable and effective tool condition monitoring (TCM) system.
The main TCM methods are direct and indirect, respectively. Since the direct methods need to be shut down to measure the tool wear value, they cannot meet the needs of industrial applications. The indirect methods analyze relevant information collected from one or more sensors to estimate tool wear conditions by using a machine-learning technique. Since the indirect methods can implement online monitoring, it has been widely adopted. The most commonly used indirect monitoring signals include cutting force signal [
2,
3,
4], vibration signal [
5,
6,
7], acoustic emission signal [
8,
9], machined surface image [
10,
11], and current signal [
12,
13,
14,
15,
16,
17].
Although the change of cutting force is closely related to tool wear conditions, the installation of the force measurement system on machine tools is difficult or unfeasible during their machining. In addition, the force measurement system is expensive—these shortcomings diminish its usefulness in real industrial applications. Compared to the force measurement system, the vibration sensor can be installed easily on machine tools. Due to the source of the vibration signal, including the impact of cutting in and out, the friction between the workpiece and cutting tool, the chip fracture and the vibration of the machine tool, and so on, vibration information that is not related to the tool wear condition is difficult to filter. In addition, the position of sensor installation affects the monitoring result greatly. Due to this, it is difficult to use the vibration signal to monitor the tool wear state accurately. Compared with the vibration signal, the frequency of the acoustic emission signal is higher, and the interference of low-frequency vibration can be eliminated; however, the acoustic emission signal may exhibit nonstationary characteristics, even if the tool state and cutting parameters are unchanged. The chip fracture and the tool’s cutting in and out can affect the acoustic emission signal; therefore, the preprocessing and feature extraction of the acoustic emission signal related to the tool wear condition is exceedingly difficult. Since the characteristics of the machined surface image are closely related to the tool wear state, the feature extraction of the machined surface image can be used to judge the tool wear status. The machined surface image can be obtained by a vision-measuring instrument. For example, an industrial camera can be applied to capture the machined surface image. However, considering the influence of the cutting fluid and chips, it is difficult to achieve accurate online TCM.
Compared with the above sensors, the motor current sensor is considered to be more suitable for the actual processing environment due to its relatively simple application and lack of installation effect on machining operations [
14]. In addition, the cost of the current sensor is cheap, and it is easy to obtain the current signal related to the tool wear state. Further, the internal system of different types of machine tools is equipped with an embedded Ethernet board card, so that users can easily connect to a computer via an Ethernet cable to obtain the current data. Especially for some open numerical control machine tools, the user can easily read the current data of the built-in current sensor through the secondary development protocol; therefore, tool wear condition monitoring based on the current signal analysis can both save costs and apply to processing environments; however, the single current sensor is less commonly used than above other types of sensors.
Although multiple sensors can get more information related to the tool wear condition than a single current sensor, the costs and the interference caused by sensors increases with an increasing amount of sensors; therefore, in recent years, some researchers have made some achievements in the application of single-current sensors for tool wear monitoring. For example, Akbari et al. [
12] indicated that the total harmonic distortion (THD) and crest factor measures of the current signal can detect tool wear degree; however, they need additional harmonic analysis equipment to calculate the THD. Further, the tool wear condition will be misjudged if the tool cuts in or cuts out of the workpiece or if the power supply system is affected by the interference signal. Mahmoud et al. [
13] utilized generalized features of the current signal to describe the tool condition, although the method proposed by them is only used to realize the binary classification of the tool wear condition, even though the feature ranking and selection is also needed. Zhou et al. [
14] proposed a TCM method based on the time, frequency, and time–frequency domains of the current signals and an identification model based on an improved kernel extreme learning machine (KELM); however, it is difficult to determine the optimal value of the two output weight vectors when the improved KELM is used for classification. Khajavi et al. [
15] used a multi-layer neural network to predict tool wear based on an analysis of the motor current signal. Since the proposed method needs an extremely rich sample for model training, it will waste a lot of manpower and material resources. Lin et al. [
16] proposed an intelligent tool breakage monitoring methodology with spindle motor current signals in the milling processing; however, they cannot achieve a multi-classification of tool wear conditions. Based on the current sensor signals, Huang et al. [
17] proposed a method of discriminant diffusion maps analysis for evaluating the tool wear status during the milling process; however, the method they proposed needs calculations of the feature dimension reduction and feature fusion. Compared to the above-mentioned TCM methods, this paper proposes a more effective and convenient TCM method, which can avoid the problems of the above monitoring methods.
As one of the most commonly used signal-processing techniques, the wavelet packet decomposition (WPD) conducts a multi-level band division over the entire signal band and further decomposes the high-frequency band to increase the frequency resolution [
14]. Since the selection of the wavelet basis function has a great influence on the decomposition result of the signal, the optimal selection of the wavelet basis function and its parameters is difficult; however, variational mode decomposition (VMD) has obvious advantages in analyzing the nonstationary signals due to its unique decomposition principle.
For example, VMD has been used to extract features from the nonstationary Bluetooth transient signals to improve classification accuracy. According to the classification results, a higher classification performance is achieved when higher-order statistical features are extracted from band-limited modes in the implementation of VMD in the radio frequency fingerprinting of Bluetooth devices [
18]. Ruicheng et al. [
19] used the VMD algorithm to extract the low-frequency displacement of the global navigation satellite system. According to the simulation and measured data, the VMD algorithm can effectively resist the modal aliasing caused by noise and discontinuous signals compared to the commonly used empirical mode decomposition (EMD) algorithm. Sahani et al. [
20] applied the VMD to the real-time identification of power quality events in electrical power systems. Through the decomposition of the harmonic signal and flicker signal in power systems, the features for power quality identification were obtained. The experimental results showed that this method has a strong, robust antinoise performance and lesser computational complexity. Xiao et al. [
21] used the VMD algorithm to decompose the original surface electromyogram signal into multiple variational mode functions (VMFs) and calculate the corresponding composite permutation entropy index (CPEI) of each signal component, and these CPEI features are applied to recognize hand actions. The experimental results show that the method based on VMD and CPEI for hand motion classification is feasible and accurate. Liu et al. [
22] adopted the VMD algorithm to extract the time–frequency domain characteristic features of the arc current signals. Compared with some traditional modal decomposition algorithms, the decomposition results show that VMD can obtain high-quality frequency bands by avoiding the modal aliasing; therefore, in this study, we implement the VMD to decompose a nonstationary current signal.
The commonly used classifiers for tool wear identification include artificial neural networks (ANNs) [
5,
23], support vector machines (SVM) [
24], hidden Markov models (HMM) [
25,
26], etc. The widely used ANNs are three-layer ANNs. Three-layer ANNs can approach any nonlinear function with arbitrary precision when the number of hidden layers is enough. The disadvantage of ANNs is that, when the sample is insufficient or the sample noise is large, it is easy to appear underfitting or overfitting. The theory of the SVM algorithm is based on the statistical learning theory; the proposed algorithm embodies the structural risk minimization principle, and it has a distinct advantage in analyzing and classifying small-scale samples. When there are only limited labeled samples, SVM has a better generalization than ANNs; however, the penalty actor and the kernel function, along with its parameters, are the main factors affecting the performance of the SVM classification performance. If the parameter selections are improper, the generalization ability of the classifier will be weakened to a large extent. An HMM is a statistical model that assumes the observation sequence is generated by a Markov process with hidden states; therefore, an HMM is a typical generative model. Compared with ANNs, SVM, and other discriminative learning models, the advantage of the generative model is that it has a stronger generalization capability; however, to obtain accurate recognition results, the generative model needs a large number of samples to study. Since the recognition effect of a single classifier is often not ideal, the advantages of ensemble learning (EL) are reflected. EL is a methodology that combines multiple individual learners and can obtain better classification accuracy and generalization ability. The random forest (RF) as a classifier developed by Breiman [
27] is an EL classifier.
Many application cases have proved that the RF algorithm has higher prediction accuracy. Wu et al. [
6] applied RF to tool wear prediction, as well as compared the performance of RF with ANNs and SVM. The experimental results showed that RF can generate more accurate recognition accuracy than ANNs and SVM. LI et al. [
28] proposed a feasible driver identification method utilizing a machine-learning algorithm with driving information. Four basic classification algorithms were performed on the datasets for comparison. The experimental results showed that the RF algorithm had the best performance on identification accuracy among the four basic algorithms. William et al. [
29] produced a dataset that contains patterns of radio wave signals obtained using software-defined radios to establish if a subject is standing up or sitting down as a test case. They used the RF model to conduct a real-time classification of a standing or sitting state based on the dataset. The results showed that the RF model can generate 96.70% classification accuracy.
The remainder of this paper is organized as follows:
Section 2 presents the method of spindle motor current signal acquisition, the method of current signal feature extraction, and the tool wear condition prediction based on ensemble learning (EL).
Section 3 presents the experimental results and discussion.
Section 4 concludes this paper with a summary of the contributions made.
4. Conclusions
In the existing research papers of online tool wear condition recognition, most researchers obtain the monitoring information related to tool wear through external sensors such as a stationary dynamometer, acoustic emission sensor, piezo accelerometers, industrial camera, and so on. Although they can realize the identification of tool wear conditions, the installation of these external sensors causes machining interference. Given this serious shortcoming, we propose a TCM method that can be applied to actual processing conditions and has a high prediction accuracy. Our work is summarized as follows:
(1) We provided a deep review of past TCM methods. The analysis and comparison show that TCM based on the spindle motor current signal is an affordable, feasible, and effective monitoring method. Then, through experimentation, we compared our proposed method and other TCM methods based on the current signal analysis. The experimental results show that using our proposed approach for TCM is more suitable and effective for the actual processing environment.
(2) The time–domain analysis, frequency–domain analysis, and VMD were jointly used to extract the sensitive features of the monitoring current signal, which is closely related to the tool wear condition. The experimental results show that the VMD has an excellent performance in processing the nonstationary current signal; the frequency band and energy features obtained by the VMD of the current signal are more sensitive to tool wear than that obtained by the frequently used WPD of the current signal.
(3) Since RF can handle thousands of input variables without variable deletion, it improves the identification efficiency. Due to this, we chose RF for tool wear condition prediction. The prediction model was established by combining the sensitive features and RF. The results show that the established tool wear prediction model has a higher prediction accuracy, smaller variance, and better robustness.