Chatter Detection In Milling Research Articles

An MLGRU and SAM-based Approach to Milling Chatter Detection using Multi-sensor Data

An MLGRU and SAM-based Approach to Milling Chatter Detection using Multi-sensor Data

In-process feature extraction of milling chatter based on second-order synchroextracting transform and fast kutrogram

In the milling process,chatter is a self-excited vibration, which has adverse effects on the quality of the workpiece. So online milling chatter detection is very important for stable and accurate machining operation. This paper proposes an in-process feature extraction method for milling chatter based on second-order synchroextracting transform and fast kutrogram. Firstly, power spectrum entropy, mean value, standard deviation and Rényi entropy are used to preliminary determine whether the collected signal is a chattering signal. Secondly, the chattering part of the collected signal is reconstructed by second-order synchroextrating transform. Thirdly, the reconstructed signal is processed by fast kutrogram to obtain kurtosis distribution. Last, the milling chatter frequency can be obtained by envelope spectral processing of the signal part with maximum kurtosis. The experiment results show that the proposed chatter diagnosis algorithms not only can detect the milling chatter online, but also can find the chatter frequency effectively.

An intelligent milling chatter detection method based on VMD-synchro-squeeze wavelet and transfer learning via deep CNN with vibration signals

An intelligent milling chatter detection method based on VMD-synchro-squeeze wavelet and transfer learning via deep CNN with vibration signals

Building digital-twin virtual machining for milling chatter detection based on VMD, synchro-squeeze wavelet, and pre-trained network CNNs with vibration signals

Building digital-twin virtual machining for milling chatter detection based on VMD, synchro-squeeze wavelet, and pre-trained network CNNs with vibration signals

Milling chatter detection based on information entropy of interval frequency

Chatter has been one of the most unfavorable issues during the milling, seriously affecting the machining efficiency, quality of machined surface. In this paper, considering the distribution characteristics of multiple chatter frequencies for milling process, a chatter detection method based on the information entropy of interval frequency (IFIE) is proposed. The matrix notch filter is first utilized to filter out the harmonics of spindle rotating frequency and possible colored noise components. Then the main components of filtered signals are estimated with a subspace-based technique. Specially, interval frequency (IF) that denotes the distance between estimated frequencies to the nearest harmonics of spindle rotating frequency is proposed, and then the chatter indicator IFIE is developed by constructing the histogram of IFs. Finally, the milling experiments under various conditions are utilized to validate the proposed methodology, and the results show that the chatter at early stage can be accurately and timely detected.

Improving generalisation and accuracy of on-line milling chatter detection via a novel hybrid deep convolutional neural network

Unstable chatter seriously reduces the quality of machined workpiece and machining efficiency. In order to improve productivity, on-line chatter detection has attracted much interest in the past decades. Nevertheless, traditional methods are inevitably flawed due to the manually extracted features. Deep learning methods possess outstanding feature learning and classification capabilities, but the generalisation and accuracy are severely affected by the labelling and training of data. To address this, this paper proposed a novel hybrid deep convolutional neural network method combining an Inception module and a Squeeze-and-Excitation ResNet block (SR-block), namely ISR-CNN. The Inception module can automatically extract multi-scale features of cutting force signal to enrich the feature map. The SR-block can assign weights to different feature channels, thus suppressing useless feature maps and improving the model accuracy. Meanwhile, the introduction of SR-block also reduces the risk of gradient disappearance and speeds up the training of network. The generalisation and accuracy of the model is guaranteed by combining the two modules without training with transition state data. Milling tests were carried out on a wedge-shaped workpiece using different cutting parameters and tool overhang lengths to verify the accuracy and generalisability of the proposed method. The results showed that the proposed method outperforms other methods by achieving classification accuracy of on the validation and test sets 100% and 97.8%, respectively. In comparison to existing methods, the proposed method can correctly identify each machining state, including the transition states. Furthermore, the proposed method identifies the onset of chatter earlier than other methods, which is beneficial for chatter suppression.

Research on milling chatter identification of thin-walled parts based on incremental learning and multi-signal fusion

Chatter is a kind of self-excited vibration that often occurs in the milling process of thin-walled parts, which has become the main factor restricting production efficiency and quality. Due to the occurrence of chatter, the signal becomes more complex and unstable. In order to realize milling chatter detection of thin-walled parts, the method of multi-sensor signal fusion is used. A chatter detection method based on variational mode decomposition (VMD) and nonlinear dimensionless index is proposed by analyzing the characteristics of signals in time–frequency domain. Firstly, a series of intrinsic mode function (IMF) components are obtained by decomposing force and acceleration signals with VMD. When chatter occurs, the energy is transferred to the chatter frequency band. Each IMF signal’s nonlinear energy entropy (EE) is extracted to construct the feature vector. A support vector machine chatter identification model based on multi-sensor signal fusion is established. To solve the problem of model incremental updating, supervised learning and unsupervised learning are combined to provide a method for chatter detection.

Investigation of chatter detection with sensor-integrated tool holders based on strain measurement

Machining chatter is one of the most critical issues that restrict the productivity in milling of thin wall workpieces. Sensor-integrated tool/tool holders, which provide data collection during cutting, can be employed for online chatter detection. Recently, there has been an increasing number of strain-measurement-based smart tool holders, which can measure bending moments and/or torque. Although accelerometer-integrated tool holders have been tested, sensor-integrated tool holders based on strain measurement have not been evaluated for the chatter detection in milling. This paper investigates the potential of chatter avoidance using a commercial sensor-integrated tool holder based on strain measurement.

Milling chatter detection of thin-walled parts based on GA-SE-SCK-VMD and RCMDE

In the milling process, it is easy to produce chatter due to the low rigidity of the thin-walled structure, which leads to the deterioration of workpiece surface quality and reduces the service life of cutting tools and machine tools. Therefore, a new chatter detection method for thin-walled parts based on optimal variational mode decomposition (OVMD) and refined composite multi-scale dispersion entropy (RCMDE) is proposed in this paper. Firstly, to solve the problem that the decomposition effect of the variational mode decomposition (VMD) algorithm is greatly affected by its parameter, a genetic algorithm (GA) is used to iteratively optimize the parameter of the VMD algorithm, and a new index, square envelope spectral correlated kurtosis (SE-SCK), is introduced as the fitness function of the genetic algorithm. Then, the energy ratio of the decomposed signal is calculated as the principle of selecting sub-components, and the sub-components with rich chatter information are selected for signal reconstruction. To solve the problem that the multi-scale dispersion entropy (MDE) will miss some information in the multi-scale process, RCMDE is introduced to detect milling chatter. Finally, the experiment of the variable cutting depth in side milling of titanium alloy thin-walled parts is carried out. The experimental results show that the OVMD algorithm proposed can solve the problem of difficult separation of chatter frequency bands caused by mode aliasing and lay a foundation for subsequent chatter feature extraction. RCMDE is more conducive to chatter detection than the single-scale DE when the scale factor is 4. The distinguishing effect of RCMDE on the machining state is more than 50% higher than that of MDE when the scale factor is 4.

Ensemble-Based Semi-Supervised Learning for Milling Chatter Detection

Chatter is one of the most deleterious phenomena during the machining process, and leads to a low quality of workpiece surface, a noisy workplace, and decreases in tool and machine life. In order to overcome these limitations and improve the machining performance, various effective methods have been developed for chatter detection. The main shortcoming of such methods is that they require all the data to be labeled. However, the labeled data that accurately reflect the chatter states are hard to collect in practical application. This paper proposes a semi-supervised method to classify chatter states with a small quantity of labeled data and large quantity of unlabeled ones. In order to improve the classification accuracy and generalization ability, ensemble learning is combined with the semi-supervised method, and an EB-SSL model is proposed in this paper. Take the non-stationarity and multiple scaling behaviors of chatter data into consideration, multifractal detrended fluctuation analysis (MF-DFA) is utilized to extract distinguished features from raw chatter detection signals. Experimental results show that this method can identify the chatter states more accurately. The performance analysis indicates that the proposed method is applicable in different milling conditions.

An unsupervised chatter detection method based on AE and merging GMM and K-means

During metal cutting, chatter is prone to the effects of poor surface quality and tool wear. Therefore, chatter detection is becoming more and more important. The current hot methods are effective, but they also have limitations, such as the interference of human experience on the results, the need to label the data, and it takes a long time. This paper proposes an unsupervised milling chatter detection method based on a large number of unlabeled dynamic signals. The method does not depend on processing parameters and environment, does not require labels, and has strong stability. Based on auto-encode, each segment of the signal is compressed into two dimensions, and the feasibility of the reconstruction scheme is verified by numerical analysis. In the normalization algorithm, the similarity between the raw signal and the reconstructed signal is the highest, and the reconstruction effect is the best. Then, the compressed signals are clustered based on a hybrid clustering method combining GMM and K-means. Under the six evaluation indicators, compared with GMM, the clustering results of this scheme have been significantly improved. The evaluation metrics show that GMM-K-means is not only more stable but also has better result compared to K-means in chatter detection. The results show that the proposed method outperforms GMM and K-means in all six typical unsupervised evaluation metrics, and can detect chatter effectively.

In-Process Chatter Detection in Milling: Comparison of the Robustness of Selected Entropy Methods

This article deals with the issue of online chatter detection during milling. The aim is to achieve a verification of the reliability and robustness of selected methods for the detection of chatter that can be evaluated on the machine tool in real time by using the accelerometer signal. In the introductory part of the paper, an overview of the current state of the art in the field of chatter detection is summarized. Entropic methods have been selected that evaluate the presence of chatter from the qualitative behavior of the signal rather than from the magnitude of its amplitude, because the latter can be affected by the transmission of vibrations to the accelerometer position. Another criterion for selection was the potential for practical implementation in a real-time evaluation of the accelerometer signal, which is nowadays quite commonly installed on machine tools. The robustness of the methods was tested with respect to tool compliance, which affects both chatter occurrence and vibration transfer to the accelerometer location. Therefore, the study was carried out on a slender milling tool with two different overhangs and on a rigid roughing tool. The reference stability assessment for each measurement was based on samples of the machined surface. The signals obtained from the accelerometer were then post-processed and used to calculate the chatter indicators. In this way, it was possible to compare different methods in terms of their ability to achieve reliable in-process detection of chatter and in terms of the computational complexity of the indicator.

Chatter detection in milling of thin-walled parts using multi-channel feature fusion and temporal attention-based network

Chatter often occurs in the milling of thin-walled parts due to their high flexibility, which can seriously impair machining accuracy, surface quality, and production efficiency. In this paper, a chatter detection method in the milling of thin-walled parts based on deep learning is proposed. Multi-channel signal features in the time, frequency, and time–frequency domains are employed and their chatter sensitivities are evaluated. A novel temporal attention-based network is constructed. The self-attention mechanism, channel attention mechanism, and attentive statistics pooling are applied to capture the intra-feature correlation, assign the weights of different features, and calculate the variations and weights of features at multiple moments, respectively. The mutual information of distinct features is integrated into the channel attention mechanism to enhance the function of suppressing useless features and enhancing useful ones. The additive angular margin loss function is applied to strengthen intra-class aggregation and inter-class separation. Milling experiments on straight-wall parts are performed, and the classification performance of this model is evaluated in three scenarios. Afterward, milling experiments on curved thin-walled parts are conducted to verify the generalization performance. The results show that the proposed method can accurately detect chatter under varying machining conditions.

Milling chatter detection with WPD and power entropy for Ti-6Al-4V thin-walled parts based on multi-source signals fusion

Unstable cutting has a relatively large negative impact on the machining quality and efficiency of thin-walled parts. It can easily cause severe vibration of milling systems, resulting in poor surface roughness of workpieces. Although some promising signal processing methods, such as wavelet packet decomposition (WPD), have been applied to process nonlinear signals, few studies have paid attention to determining a wavelet basis and the number of decomposition layers in WPD. Parameters of WPD play a crucial role and they are often empirically determined. In this paper, an adaptive denoising model based on WPD and recursive least squares with a variable forgetting factor (RLSVFF) is firstly established, and the influence of the forgetting factor in the model on signal denoising are investigated. Then, a novel chatter detection method based on multi-source signals fusion using WPD and power entropy is presented. On this basis, an automated selection method based on a margin indicator and power is proposed for applications to WPD. And the influence of different parameters of WPD on a margin indicator and power are investigated. In order to improve the efficiency of different levels of signal acquisition, a method based on a stability lobe diagram (SLD) is used to design experimental parameters. Compared with traditional denoising models and chatter detection methods based on a single signal, simulation and experimental results show that the adaptive denoising model and chatter detection method based on multi-source signals fusion proposed in this paper can more reliably detect the occurrence of early chatter and different levels of chatter.

An Evaluation Study of EMD, EEMD, and VMD For Chatter Detection in Milling

In modern machining processes, chatter is an inherent phenomenon that hinders efficiency, productivity, and automation. Numerous methods have been proposed using analytical, computational, and artificial intelligence methods to detect and avoid chatter during milling. The vibration signals generated during machining are of non-stationary and non-linearity nature. Hence solely time or frequency domain analysis are not adequate methods for chatter detection. This study investigates the performance of more advanced mode decomposition methods and compares them. Three decomposition methods, namely, empirical mode decomposition (EMD), ensemble empirical mode decomposition (EEMD), and variational mode decomposition (VMD), are used to decompose and identify chatter frequency bands. After decomposition, Hilbert-Huang transform (HHT) was applied for visualization. The comparative results indicate that EEMD or VMD decomposition methods performed better than EMD for intelligent chatter detection.

Effective multi-sensor data fusion for chatter detection in milling process

This paper introduces a newly developed multi-sensor data fusion for the milling chatter detection with a cheap and easy implementation compared with traditional chatter detection schemes. The proposed multi-sensor data fusion utilizes microphone and accelerometer sensors to measure the occurrence of chatter during the milling process. It has the advantageous over the dynamometer in terms of easy installation and low cost. In this paper, the wavelet packet decomposition is adopted to analyze both measured sound and vibration signals. However, the parameters of the wavelet packet decomposition require fine-tuning to provide good performance. Hence the result of the developed scheme has been improved by optimizing the selection of the wavelet packet decomposition parameters including the mother wavelet and the decomposition level based on the kurtosis and crest factors. Furthermore, the important chatter features are selected using the recursive feature elimination method, and its performance is compared with metaheuristic algorithms. Finally, several machine learning techniques have been adopted to classify the cutting stabilities based on the selected features. The results confirm that the proposed multi-sensor data fusion scheme can provide an effective chatter detection under industrial conditions, and it has higher accuracy than the traditional schemes.

Milling chatter detection by multi-feature fusion and Adaboost-SVM

Unstable chatter vibration in the milling process significantly affect the machining quality and efficiency. In order to suppress or avoid the chatter vibration in the cutting operation, detection of chatter onset is highly needed. Until now, most of the existing chatter detection methods designed chatter indicators by extracting signal features, and the threshold of designed chatter indicator is usually needed, which is difficult to determine and might not be applicable in different cutting conditions. In fact, chatter detection is essentially a typical classification problem, hence milling chatter detection based on machine learning method is presented in this paper. In order to obtain the needed data set, milling experiments under different cutting conditions were performed. Multi-features are utilized for the chatter detection, including the dimensionless features in time domain and frequency domain, and the automatic features extracted by stacked-denoising autoencoder (SDAE). In order to improve the accuracy of chatter classification and avoid the negative effects of possible samples with wrong labels, adaptive boosting (Adaboost) algorithm that consists of a series of weak classifiers by support vector machine (SVM) is utilized and further improved. Experimental verification and performance analysis are also performed, and the results show that the presented method can detect the chatter with a high accuracy and is applicable in different milling conditions.

A kMap optimized VMD-SVM model for milling chatter detection with an industrial robot

Industrial robots play an important role in the milling of large complex parts. However, the robot is less rigid and prone to vibration-related problems; chatter, which affects machining quality and efficiency, is more complex and difficult to monitor. In this paper, a variational mode decomposition-support vector machine (VMD-SVM) model based on information entropy (IE) is built to detect chatter in robotic milling. Significantly, the vibration signals are classified into four states for the first time: stable, transition, regular chatter, and irregular chatter. To improve the accuracy of the identification model based on VMD-SVM, a novel hyper-parameter optimization strategy—the kMap method—is proposed in this paper for optimizing three-dimensional hyper-parameters in the VMD-SVM model. The hyper-parameters of VMD-SVM are jointly optimized by the kMap method, with constant step sizes. As an improved grid search (GS), kMap reduces the operation time to the same order of magnitude as the heuristic algorithm (HA) [comprising particle swarm optimization (PSO) and genetic algorithm (GA)]. The VMD-SVM model with the hyper-parameters optimized by kMap exhibits higher accuracy and better stability than the hyper-parameters optimized by PSO and GA. The results of the validation experiments show that the kMap-optimized identification model is effective in industrial robotic milling.

Milling chatter detection based on VMD and difference of power spectral entropy

Chatter is a kind of unstable vibration in high-speed milling process, leading to poor surface quality of workpiece, significant tool wear, and severe noise. In order to avoid these negative effects of milling chatter, the detection of chatter at early stage is highly needed. In this paper, an early-stage chatter detection method based on variational mode decomposition (VMD) and difference of power spectral entropy (ΔPSE) is presented. Considering that the existence of possible colored noise in the monitoring signals, which might lead to the misjudgment of chatter detection, the signals monitored at spindle’s idling is utilized to identify these noise components. In order to separate the needed chatter-sensitive sub-signals, VMD is utilized to decompose the original signals into a series of intrinsic mode functions (IMFs), and the chatter-sensitive sub-signals are obtained by adding the IMFs whose central frequencies are closed to the milling system’s natural frequency. After that, an adaptive filter is utilized to filter out the harmonics of spindle-speed frequency and the identified colored noise components. Then, a dimensionless indicator is designed, which is determined as the difference of power spectral entropy (ΔPSE) of signals without and with filtering. A series of experiments are also performed, and the results indicate that the presented methodology can detect the chatter at early stage and is applicable in different cutting conditions, which is very important in the practical application.

Correction to: EEMD-based online milling chatter detection by fractal dimension and power spectral entropy

Correction to: EEMD-based online milling chatter detection by fractal dimension and power spectral entropy