Milling Chatter Research Articles

Research on the influence of cutter overhang length on robotic milling chatter stability

Serial industrial robots are widely in demand in the field of large-scale component processing and manufacturing. However, the structure characteristic of serial robots makes it easy to generate chatter during milling and the overhang length of milling cutter has an important influence on the stability of robotic milling. Milling cutter overhang length refers to the length from the tip of the cutter to the protrusion of the spindle, to study the influence of milling cutter overhang length on the stability behavior of robotic milling, the modal parameters of milling cutter under different overhang length were obtained by hammer experiment, and its dynamic characteristics were analyzed. The stability behavior for robotic milling under different cutter overhang lengths was analyzed by using the stability lobe diagrams, and the stability lobe diagrams were verified by robotic milling experiments. The results show that the rigidity and natural frequency of milling cutter decrease with the increase of overhang length. There is a big gap between the stability lobe diagrams and the actual machining state when the cutter overhang length is short, and the low frequency vibration is easy to occur in the robotic milling process when the spindle speed is low, which is most probably because that the absorbed vibration energy by the cutter has been transferred to the robot body before the milling cutter vibrates. When the cutter overhang is increased and the feed direction is along the y-axis of the robot global coordinate system, the stability lobe diagrams by considering the multi-mode coupling effect is more consistent with the actual milling state.

Study on Self-Learning System for Milling Chatter Feature Based on Hybrid Preprocessing Model and Improved Convolutional Clustering

Milling chatter is a self-excited vibration phenomenon during the cutting process, typically occurring in machining operations with lower stiffness. It significantly impacts the machining precision and surface morphology of the workpiece. To summarize the vibration signal features under operating conditions and enhance the precision of the intelligent recognition model for milling chatter features, this paper proposes an adaptive learning model for chatter feature recognition based on the improved convolutional clustering algorithm. This study uses wavelet analysis to extract chatter features and improved convolutional clustering to identify chatter and stable cutting signals. This method selected appropriate mapping techniques based on differences in chatter signals under various conditions and uses convolution for feature extraction. Improve convolutional clustering evaluates sample similarity through energy transfer, less influenced by signal space structure compared to Euclidean or Mahalanobis distances. This method provides a unified criterion for evaluating different signal feature representation spaces, thereby improving the accuracy and efficiency of chatter sample recognition. The recognition accuracy of chatter phenomena at different spindle speed ranges reaches $95$ % and $96.3$ % respectively. The results show that this method can effectively distinguish between chatter signals and stable cutting signals.

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

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%.

Influence of different position modal parameters on milling chatter stability of orthopedic surgery robots

This research is dedicated to exploring the dynamics of milling chatter stability in orthopedic surgery robots, focusing on the impact of position modal parameters on chatter stability. Initially, we develop a dynamic milling force model for the robotic milling process that integrates both modal coupling and regenerative effects. We then employ the zero-order frequency domain method to derive a chatter stability domain model, visually represented through stability lobe diagrams (SLDs). Through conducting hammer test experiments, we ascertain the robot's modal parameters at varying positions, enabling the precise generation of SLDs. This study also includes experimental validation of the chatter SLD analysis method, laying the groundwork for further examination of chatter stability across different positional modal parameters. Finally, our analysis of the variations in modal parameters on the stability of robot milling chatter yields a theoretical framework for optimizing cutting parameters and developing control strategies within the context of orthopedic surgery robots.

A novel mode coupling mechanism for predicting low-frequency chatter in robotic milling by providing a vibration feedback perspective

The low-frequency chatter (LFC) of the robot body during milling severely constrains the machining efficiency. Taking the classical mode coupling mechanism as the underlying foundation, previous studies have developed various stability models to predict the LFC in robotic milling. However, the classical mode coupling mechanism has recently been proven to be applicable only to operations such as threading and boring, showing significant deviations in robotic milling. In response, this paper proposes a novel vibration feedback-based mode coupling (VF-MC) mechanism applicable to robotic milling based on robotic modal directionality, which describes the coupling and feedback process of modal vibration among multiple modes through dynamic cutting forces. The feedback process is modeled considering the time delay effect. The LFC can be judged by the trend of the amplitudes of the feedback vibrations. The effectiveness and efficiency of the VF-MC model for predicting the LFC are verified by comparing with the milling experiment results and the traditional mode coupling chatter model. Furthermore, the dynamical conditions for the LFC are investigated based on the VF-MC mechanism with the experimental evidence. The core contribution of this paper is to quantitatively reveal the feedback mechanism of low-frequency self-excited vibration among modes in robotic milling, and further develop the VF-MC model to efficiently predict the LFC. The code for the VF-MC model is available at https://github.com/Dr-JwWu/VF-MC__A-chatter-model-for-robotic-milling.

Stability prediction for robotic milling based on tool tip frequency response prediction by considering the interface stiffness of spindle-tool system

The change of tool tip frequency response caused by the posture dependence of robot dynamic is one of the key problems that make it difficult to accurately predict the milling stability of robot. In this paper, a tool tip frequency response prediction method considering the interface stiffness characteristics of spindle-tool system is proposed for stability prediction of robotic milling under the condition of posture variation. Firstly, the interface stiffness models of spindle-toolholder, toolholder-spring clip and spring clip-tool are established based on Yoshimura's unit area method. Then, The dynamics model for the robot body and the interface stiffness models for spindle-tool system are imported into the finite element analysis model of the spindle system, so that the prediction of the tool tip frequency response is realized by harmonic response analysis. Compared with the experimental results, the maximum error of the natural frequency was not more than 2 %, and the maximum error of the amplitude was not more than 12 %. Finally, the 2 DOF robot milling stability prediction model is established. Then the robot milling chatter is predicted considering redundant degrees of freedom from the perspective of regenerative chatter prediction theory, and the accuracy of prediction results is verified by milling experiment.

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.

Stability Analysis in Milling Based on the Localized Differential Quadrature Method.

Chatter stability analysis is an effective way to optimize the cutting parameters and achieve chatter-free machining. This paper proposes a milling chatter stability analysis method based on the localized differential quadrature method (LDQM), which has the advantages of simple principle, easy application, and high computational efficiency. The milling process, considering the regeneration effect, is modeled using linear periodic delay differential equations (DDE), then the state transition matrix during the adjacent cutting period is constructed based on the LDQM, and finally, the stability of the milling process is obtained based on the Floquet theory. The accuracy and computation efficiency of the proposed method are verified through two benchmark milling models. The simulation results demonstrate that the proposed method in this paper can accurately and quickly obtain the chatter stability lobe diagram (SLD) of the milling process, which will provide guidance for optimizing the process parameters.

Chatter failure comparison for high-speed milling of aerospace GH4099 with silicon nitride ceramic end mills: A laboratory scale investigation

Chatter generated in the high-speed cutting process can seriously limit the machining efficiency and affect the surface machining quality, which urgently needs to be controlled. Aiming at brittle ceramic end mills that are very sensitive to vibration, a strategy based on the modal experimental method is proposed to construct effective and accurate stability lobe diagram (SLD) for chatter prediction and failure analysis in this work. Firstly, the tool tip frequency response function (FRF) of four kinds of self-developed ceramic end mills and commercial cemented carbide end mills with the same structure are established by means of hammering modal experiments, and the modal parameters are solved and identified. Then, based on our established high-speed milling chatter prediction model, the milling force coefficients are obtained by linear regression analysis and calculation, thereby the milling SLDs of five end milling tools are developed. Finally, a series of high-speed side milling experiments are carried out using consistent cutting conditions. The results show that the constructed SLDs can effectively predict the chatter failure region and milling stability for five kinds of end mills, and four ceramic milling tools exhibit more excellent chatter suppression performance according to frequency domain analysis of vibration signals and the observation of workpiece surface vibration marks, in which the S4 ceramic milling tool presents the most significant high-speed milling stability.

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.

Suppressing Milling Chatter of Thin-Walled Parts by Eddy Current Dampers

Machining vibrations often occur when working with thin-walled workpieces. One effective method to mitigate these vibrations is by using a damper, which can enhance machining accuracy, surface finish, and tool life. However, traditional contact dampers have a drawback in that they require direct contact with the workpiece, leading to friction, wear, increased cutting forces, and reduced machining accuracy. In contrast, electromagnetic eddy current dampers are noncontact dampers that can effectively suppress machining vibrations without the need for physical contact. In this study, a method to suppress machining vibrations in thin-walled workpieces using electromagnetic eddy current dampers is proposed. By establishing a theoretical model for the electromagnetic damper, the damping force and equivalent damping of the damper are determined. Subsequently, the impact of electromagnetic dampers on frequency response functions and machining vibrations are investigated through hammer impact tests. The results indicate that increasing the surface damper voltage and reducing the air gap both enhance the equivalent damping of the electromagnetic eddy current damper. Moreover, cutting experiments are conducted to analyze the surface roughness of thin-walled workpieces with and without dampers. The results demonstrate that the eddy current damper can effectively increase the equivalent damping and provide the necessary damping force to suppress machining chatter. Overall, the proposed method utilizing electromagnetic eddy current dampers presents a promising solution for suppressing machining vibrations in thin-walled workpieces.

Prediction of time-varying dynamics and chatter stability analysis for surface milling of thin-walled curved CFRP workpiece

Machining curved surfaces for thin-walled components is always challenging. Due to the strong anisotropic features of carbon fiber reinforced plastic (CFRP), new challenges, such as prediction accuracy loss, have emerged for avoiding the chatter of machining thin-walled curved CFRP workpieces. This paper studied the influence of the anisotropic properties of CFRP workpieces and time-varying dynamics on surface milling stability. An improved damping and time-varying dynamics prediction model was proposed integrated with FEA and numerical calculation considering the change of cutting position, the materials removed, and the anisotropic features of CFRP. Then, the 3D stability lobe diagram (SLD) was achieved by considering the dynamic change at each cutter point on the machining path and dynamic cutting forces based on the instantaneous fiber cutting angle. Based on the obtained 3D SLD, CFRP milling chatter could be accurately avoided, and machining efficiency could be improved. A case study of milling a thin-walled toroidal surface was conducted for validation. In the machining process of CFRP, unlike homogeneous materials such as metals, the natural frequency of CFRP fluctuates nonlinearly with the change curve of material removal within a specific range. Under a higher material removal rate, the surface roughness of the machined surface has been reduced by 74%, Ra below 2.35 µm, based on the selected process parameters of the novel-established SLD, compared to the surface roughness, Ra close to 9.21 µm, obtained by the previous method without considering the time-varying dynamics.

Adaptive milling chatter identification based on sparse dictionary considering noise estimation and critical bandwidth analysis

As one of the most unfavorable factors, chatter will seriously hinder the improvement of production efficiency. The weak chatter identification is an effective method for stable cutting. In this paper, a weak chatter identification method is developed using sparse dictionary on account of the milling dynamic responses. As the bases for chatter identification, chatter frequencies are calculated based on the milling dynamic equations. In order to decrease false alarm rate due to the measured background noise, the noise estimation based filtering is proposed. Besides, in consideration of the parameter uncertainty, an adaptive critical bandwidth analysis is implemented with the developed energy proportion index in case of missed alarm. Then, the sparse dictionary matrix is constructed based on the obtained chatter frequencies and critical bandwidth. Finally, the gradient projection for sparse reconstruction (GPSR) algorithm is adopted for chatter frequencies extraction. The proposed method can decrease the measured background noise, obtain critical chatter band and extract weak chatter frequencies accurately. Experimental results shows that the developed method can be successful in weak chatter identification.

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

Research on milling chatter monitoring and suppression based on IWPEE and VASS dual indicators

Research on milling chatter monitoring and suppression based on IWPEE and VASS dual indicators

Investigation of the low-frequency chatter in robotic milling

In robotic milling with large allowance process, low-frequency chatter (LFC) is an important factor observed in high-speed and low-speed milling, affecting the processing efficiency and quality. Previous research has used the regenerative chatter theory, ignoring modulated tool-workpiece engagement conditions, or mode coupling theory under the assumption of threading operations to explain the LFC mechanism and predict the stability boundary. However, these models overlook or inaccurately characterize the modulation effect, leading to inaccurate modeling of dynamic chip thickness changes during milling, making it difficult to understand the mechanism of LFC. Here, we propose an LFC stability model that considers modulated tool-workpiece engagement conditions and the mode coupling effect of the robotic structure for robotic milling. This approach allows us to reveal the mechanism of LFC and identify the characteristic signal of low-frequency vibration, which is the sideband frequency signal. Initially, the evolution of LFC is analyzed, and its characteristics are summarized. Further, a surface renewal (SR) model is proposed to accurately calculate the dynamic cutting force caused by modulated tool-workpiece engagement conditions in LFC. Furthermore, the LFC stability model, considering the modulated tool-workpiece engagement conditions and mode coupling effect, is established based on impulse response function (IRF) method. Finally, we verify the accuracy of our model through milling experiments and compare it with that of the classical stability prediction model. Our results show that LFC is highly dependent on speed, and our stability model can effectively predict the stability boundary of LFC in robotic milling with large allowance process.

Dynamic Modeling and Stability Prediction of Robot Milling Considering the Influence of Force-Induced Deformation on Regenerative Effect and Process Damping

Undesirable chatter is one of the key problems that restrict the improvement of robot milling quality and efficiency. The prediction of chatter stability, which is used to guide the selection of process parameters, is an effective method to avoid chatter in robot milling. Due to the weak stiffness of the robot, deformation caused by milling forces becomes an unavoidable problem, which will change the tool–workpiece contact area and affect the stability prediction. However, it is often simplified and neglected. In this paper, a multipoint contact dynamic model of robot milling is established, which considers the influence of force-induced deformation on the regenerative effect and process damping. The tool–workpiece contact area is discretized into a finite number of nodes along the axial direction so that the force and deformation at each node can be calculated separately. The different contact forms of the tool–workpiece under different process parameters are discussed in different cases, and the interaction process between cutting force and force-induced deformation is analyzed in detail. An iterative strategy is used to calculate the deformation of each node and the result of the tool–workpiece contact boundary. Finally, chatter stability of robot milling is predicted by a fully discrete method. Robot milling experiments were carried out to verify the predicted results. The results show that force-induced deformation is an important factor improving the stability prediction accuracy of robot milling, and a more accurate prediction result can be obtained by simultaneously considering force-induced deformation and process damping.

Multi-step-ahead prediction of cold rolling chatter state based on the combination of Functional Data Analysis and General Autoregression Model

Mill chatter seriously restricts the improvement of production efficiency and the development of new products. Thus, a method of cold rolling chatter monitoring and early warning is proposed based on the combination of Functional Data Analysis (FDA) and General Autoregression Model (GAM). Firstly, the multi-source cold rolling data are preprocessed by FDA to realize the smooth fitting of the unequally sampled data and the sample space is constructed based on chatter mechanism. Then, the multi-step-ahead prediction of cold rolling chatter is executed through different machine learning algorithms based on GAM, and the prediction effects of different algorithms are compared and evaluated. Finally, the maximum prediction step is defined to select the optimal algorithm, and the conclusion is drawn that Extra Tree Regression (ETR) has the best prediction effect. Therefore, the proposed method for cold rolling chatter monitoring can effectively solve the problems of delay, false alarm and distortion in prediction.

Suppress chatter in milling of thin-walled parts via fixture with active support

In this study, we designed an integrated fixture system using eddy current sensors and piezoelectric actuators. With this system, active supporting forces are exerted on thin-walled workpieces to adjust their stiffness in a real-time manner and thus to suppress chatter in milling. When modeling this system, we used a mass–stiffness–damping element to characterize the dynamic behavior of the actuator under high-speed responses, and we considered the electromechanical coupling between the mechanical elements and the drive circuit of the actuator. We then proposed an optimal delayed state feedback controller for the closed-loop system. The delayed state is obtained by introducing the time delay in milling dynamics into the regular state feedback, and the optimal gain is worked out through the differential quadrature and gradient descent, which is a much more computationally efficient method than the classic semi-discretization. Among all similar approaches, our strategy leads to the largest stability region for milling of thin-walled parts and requires the least energy input, which are proved by simulations and experiments.