Chatter Detection Research Articles

Order-difference of normalized square envelope spectrum and its applications in early chatter detection of roll grinder

Roll grinder is widely demanded in grinding the surface of rolls that will be used to mill steel strips for high-quality automobile outer panels. The grinding chatter generally leads to poor machining accuracy and massive economic losses. The significance of roll grinder vibration monitoring is to detect the early grinding chatter. However, in harsh working conditions, some chatter detection methods inevitably confuse the fault components and interference components in vibration signals and the chatter feature information cannot be extracted exactly. The dynamical model of roll grinder system reveals that the spectrums of vibration signals contain intrinsic frequency components and chatter frequency components under health and chatter conditions. Based on the analytical modelling, a new health index order-difference of normalized square envelope spectrum (ODNSES) is proposed to detect early chatter and evaluate the health degradation of the roll grinder. The normalized square envelope spectrum (SES) is used as the pretreatment of ODNSES to compare the frequency components in vibration signals under health and early chatter conditions. Furthermore, order-difference of normalized SES is proposed to suppress the noise interference and to enable ODNSES to characterize the grinding chatter process. The detailed simulation research is performed to verify three important properties of ODNSES. The experimental results show that ODNSES would not be interfered by background noise significantly and it is on average 29.12% more sensitivity to impulse amplitudes than classical health indexes. The testing results on actual roll grinder exhibit that ODNSES can detect the early chatter and quantify the abnormal vibration in the grinding process.

Online real-time machining chatter sound detection using convolutional neural network by adopting expert knowledge

In machining processes, chatter detection remains a pivotal challenge, impacting both the quality and efficiency of manufacturing operations. This study introduces an approach that synergizes expert knowledge with the capabilities of advanced convolutional neural networks (CNNs) to enhance chatter detection. A comprehensive monitoring framework is proposed to adopt expert knowledge that digitizes machine tool and sound data, effectively labeling chatter events. This study merges human expertise in identifying milling tool chatter sounds with CNN architecture, marking a notable advancement in blending machining insights of experts with modern artificial intelligence (AI) technologies. The proposed chatter prediction architecture is distinguished by its incorporation of an attention block, fusing outputs from the AlexNet model with cutting parameters. This model outshines baseline models in both in-distribution (ID) and out-of-distribution (OOD) testing datasets. In OOD testing, the proposed model achieved an impressive accuracy of 94.51%, markedly surpassing the standalone CNN model’s accuracy of 88.66%. Real-time 3D visualization of machining operations is demonstrated through the successful implementation of the trained model on a Raspberry Pi.

A feature extraction method for intelligent chatter detection in the milling process

A feature extraction method for intelligent chatter detection in the milling process

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

Online chatter detection via lightweight deep learning framework with efficient signal pre-processing

Deep learning methods have attracted considerable attention in the field of chatter detection due to their exceptional feature extraction and identification capabilities. However, it is challenging to adapt these methods to diverse machining conditions with limited sensor resources. In addition, the computational efficiency of these methods is compromised by their complexity, resulting in a reduced real-time performance for online chatter detection. To address them, this paper proposes a novel deep learning-based framework that combines an efficient signal pre-processing method with a novel lightweight parallel convolutional neural network (LPNN). A fast continuous wavelet transform algorithm is applied to the raw signal, effectively generating time–frequency maps that represent chatter characteristics. The LPNN incorporates the condensed block as a feature extraction unit, which is constructed using depthwise separable convolution. The introduction of the condensed block and the parallel structure increases the depth and width of the designed model while maintaining fewer parameters and higher computational efficiency. A transfer learning strategy is adopted to train the model, reducing the training time and data requirements for online chatter detection. Experimental results using large open-source turning datasets demonstrate the effectiveness of the proposed method in identifying chatter under various machining conditions. The proposed method outperforms other methods with a classification accuracy of 96.5% on the test set. Furthermore, the performance of the proposed method for online chatter detection is validated in our milling experiments, exhibiting enhanced real-time performance compared to other methods.

Multi-Sensory Tool Holder for Process Force Monitoring and Chatter Detection in Milling.

Sensor-based monitoring of process and tool condition in milling is a key technology for improving productivity and workpiece quality, as well as enabling automation of machine tools. However, industrial implementation of such monitoring systems remains a difficult task, since they require high sensitivity and minimal impact on CNC machines and cutting conditions. This paper presents a novel multi-sensory tool holder for measurement of process forces and vibrations in direct proximity to the cutting tool. In particular, the sensor system has an integrated temperature sensor, a triaxial accelerometer and strain gauges for measurement of axial force and bending moment. It is equipped with a self-sufficient electric generator and wireless data transmission, allowing for a tool holder design without interfering contours. Milling and drilling experiments with varying cutting parameters are conducted. The measurement data are analyzed, pre-processed and verified with reference signals. Furthermore, the suitability of all integrated sensors for detection of dynamic instabilities (chatter) is investigated, showing that bending moment and tangential acceleration signals are the most sensitive regarding this monitoring task.

An adaptive chatter detection method for micro milling based on variational mode extraction

Current chatter detection methods using variational mode decomposition (VMD) hardly select appropriate intrinsic mode functions (IMFs) for characterizing chatter in micro milling. This article introduces a novel variational mode extraction (VME) method to isolate a single IMF representing chatter. An adaptive filter reduces chatter-independent components, enabling VME to identify the desired IMF with adaptive calculations for center frequency and penalty factor. The Hilbert–Huang transform is then applied to obtain the time–frequency distribution of the obtained IMF, and various features are computed. Finally, two methods are employed: streaming feature selection considers feature interaction, and radial basis function support vector machine selects pertinent features to establish the chatter detection model. Extensive micro milling tests validate the method, achieving an average detection accuracy of 99.34%.

An effective investigation of chatter prediction system on Al6061 alloy in an end milling process

Visual examination of the surface topography, in conjunction with the other sensors, may confirm the existence of chatter. Online chatter detection during real machining operations is possible with the use of sensors, and the presence of noise in their output and restricted bandwidth are the major drawbacks of these sensors. Productivity drops and manufacturing costs go up when there is a lot of chatter in the machining process. In the present paper, an integrated spindle tool system is modeled using finite element method with Timoshenko beam theory including rotational and shear deformation effects. To maximize the average stable depth of cut in an end milling process while simultaneously minimizing the chatter vibration levels, real time and offline strategies have been investigated. Machining experiments performed on Al6061-alloy specimens provide an empirical confirmation of the stability boundaries. The surface topography methods such as scanning electron microscope (SEM) and optical microscope images along with vibration levels are considered, to identify chatter marks under various machining conditions, which helps to assure cutting process stability. Stability lobe diagrams are plotted with these derived conditions and observed at an incremental level in the axial depths of the cut. The methodology shown in this paper improves the machining stability of the end milling with the reduction in the tool tip vibrations.

Real-time chatter detection during turning operation using wavelet scattering network

Real-time chatter detection during turning operation using wavelet scattering network

Chatter detection in simulated machining data: a simple refined approach to vibration data

Vibration monitoring is a critical aspect of assessing the health and performance of machinery and industrial processes. This study explores the application of machine learning techniques, specifically the Random Forest (RF) classification model, to predict and classify chatter—a detrimental self-excited vibration phenomenon—during machining operations. While sophisticated methods have been employed to address chatter, this research investigates the efficacy of a novel approach to an RF model. The study leverages simulated vibration data, bypassing resource-intensive real-world data collection, to develop a versatile chatter detection model applicable across diverse machining configurations. The feature extraction process combines time-series features and Fast Fourier Transform (FFT) data features, streamlining the model while addressing challenges posed by feature selection. By focusing on the RF model’s simplicity and efficiency, this research advances chatter detection techniques, offering a practical tool with improved generalizability, computational efficiency, and ease of interpretation. The study demonstrates that innovation can reside in simplicity, opening avenues for wider applicability and accelerated progress in the machining industry.

Chatter Detection Approach Based on Wavelet Synchrosqueezing and t-Distributed Stochastic Neighbor Embedding for a Turning Process

Chatter Detection Approach Based on Wavelet Synchrosqueezing and t-Distributed Stochastic Neighbor Embedding for a Turning Process

Multitype chatter detection via multichannelinternal and external signals in robotic milling

Robot milling chatter monitoring is susceptible to interference from robot pose, feed direction, inertial excitation-dominated low-frequency flexible vibration (IELFFV), and spindle noise. Therefore, this paper proposes a multi-type chatter detection method utilizing multi-channel internal and external signals. First, the multichannel vibration signals and internal controller signals were acquired and aligned. The vibration signals were converted into an engagement coordinate system. Then, based on the milling vibration signal characterization and sensitivity analysis of vibration signals to chatter, a combination of three chatter indicators, calculated via filtered displacement, filtered acceleration, and internal signals, was utilized to detect structural mode-dominated low-frequency chatter, IELFFV, and tool mode-dominated high-frequency chatter. Finally, the effectiveness of the proposed method in detecting the multitype chatter was demonstrated via robot milling experiments. However, the process of the tool entering and exiting the workpiece could be misclassification as TMHFC, which would be addressed in subsequent research.

CONSIDERATIONS FOR THE PARAMETERISATION OF A CHATTER DETECTION ALGORITHM

Process instabilites due to the regenerative effect limit the material removal rate of cutting machine tools. A method developed at the Laboratory for Machine Tools and Production Engineering (WZL) at RWTH Aachen University reduces the experimental effort of determining stability limits for milling. The method bears on a time-domain chatter detection algorithm, which detects process instability based on predefined threshold values. Up to now, the definition of the threshold values has been derived from a selective number of sample tests. To investigate the transferability of the chatter detection algorithm to various boundary conditions cutting tests are carried out. In addition, a new stability criterion for high frequency chatter oscillations is introduced. The results are analysed with regard to factors that require an adjustment of the threshold values. Based on the results, a parameterisation of the chatter detection algorithm is presented.

A novel chatter detection method in micro-milling process using wavelet packet entropy

A novel chatter detection method in micro-milling process using wavelet packet entropy

Chatter detection and suppression in machining processes: a comprehensive analysis

Chatter detection and suppression in machining processes: a comprehensive analysis

Local fractional Duffing equation: Its periodic property and its application to energy harvesting

A local fractional modification of the Duffing equation is considered, and the homotopy perturbation method is employed to reveal its frequency-amplitude relationship, which is of paramount importance in the optimal design of the energy harvesting devices and chatter detection. Effects of the initial conditions on the periodic property is also discussed.

Real-time chatter detection based on fast recursive variational mode decomposition

Real-time chatter detection based on fast recursive variational mode decomposition

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.

Improved STFT analysis using time-frequency masking for chatter detection in the milling process

In the field of machining, the purposeful increase in cutting depth and rotation speed can potentially cause chatter vibration. Chatter lowers surface quality, tool and spindle life, and produces excessive noise, which limits the productivity of the machining process. In this paper, an improved Short-Time Fourier Transform (STFT) involving time–frequency masking and adaptive thresholding is proposed. Initially, hammering tests were undertaken to ascertain the inherent natural frequency of the cutting system. Subsequently, milling tests were conducted across varied conditions utilizing an accelerometer sensor and a microphone (capturing sound signals) to acquire the requisite experimental data. A 3-level Variational Mode Decomposition (VMD) was used for pre-processing due to its inherentcapabilities for noise reduction. The chatter threshold is established by averaging the total intrinsic mode function (IMF) energy levels that have been selected. To demonstrate the efficacy of the proposed method, root mean square (RMS) and skewness were calculated as chatter indicators. The improved STFT time–frequency representation (TFR) was compared to the conventional TFR. Notably, both indicators demonstrate effectiveness and chatter sensitivity, while the improved STFT offers greater time–frequency resolution than its conventional counterpart and can be successfully used for monitoring the milling process.

CHATTER DETECTION IN VIBRATION SIGNALS USING TIME-FREQUENCY ANALYSIS

Chatter is a common state in the end milling, which has major influence on machining quality. Early chatter detection is a prerequisite for taking adequate measures to avoid chatter. Nevertheless, there are still numerous challenges and difficulties in the feature extraction of chatter detection. In this paper, effective chatter detection in the milling process of a vibration signal is investigated using time frequency analysis. Firstly, the measured vibration signal in the machining process was preprocessed by Wavelet Transform decomposition. Different sub-bands were obtained and the portion with high chatter information was reconstructed for further analysis. Since measured signals from sensors usually constitute of background noise and other disturbances, the wavelet decomposition serves as a preprocessor to denoise the measured signals and enhance the performance of the Wavelet Synchro Squeezing Transform (WSST) which was applied on the reconstructed signal for chatter Identification. The techniques were used to detect the chatter in different operating condition of the machining process. Finally, some milling tests were conducted and the experiment results prove that the proposed method indeed succeed in effectively chatter identification.