Onset Of Chatter Research Articles

Development of smart manufacturing framework for micromilling of thin-walled Ti6Al4V

Identification of chatter is mostly carried out offline after capturing the machining signals or by analyzing the machined surface after completing or interrupting the machining process. The offline methods for analyzing the machined signals are tedious and not very useful for microcomponents attributed to permanent deformation of thin-wall if chatter has already been occurred during process. Real-time information is necessary to monitor and control the micromilling process. Consequently, a smart manufacturing framework has been developed in the present work to monitor the chatter onset and the deviation in geometrical shape of the thin-wall during micromilling. An interactive web interface has been developed to project the real-time information necessary to monitor the micromilling process. The excitation of natural modes of the thin-walled structure of Ti6Al4V has been observed in real-time on the web browser for micromilling at 80 µm radial depth of cut, 50,000 rpm and a feed rate of 3 µm/flute. The maximum permanent deformation of 97.43 µm for thin-wall has been obtained at 130 µm radial depth of cut. The developed layout of web browser can be used for real-time monitoring of any other machining process as well.

Influence of microphone tilt angle on instability identification in micromilling of thin-walled Ti6Al4V

High speed micromilling is the most preferred manufacturing process to fabricate complex 3D featured thin-walled structures like stents, turbine blades, micro-fins, cabin parts etc., that are extensively used in bio-medical, energy, automobile and aerospace sectors. Fabricating thin-walled structures with high dimensional and geometrical accuracy requires chatter free machining parameters. Different sensors like accelerometer, current sensors, laser based sensors, vibration pick-ups etc. are available for capturing the vibration while machining thin-walled structures. Each sensor has its own advantages and disadvantages, like laser based sensors adds noise to response as chips fly past in the laser path and accelerometer captures deflection of both micro-cutting tool and thin-walled workpiece. Hence, In-process chatter identification is a challenging task in thin-wall micromilling. Also, the Filtering of signals to remove the noise from signals captured via contact type and laser based sensors results in loss of low-amplitude chatter data in high-speed micromilling. Consequently, there is a need of sensor, which functions similar to contact type with less or no loss in low-amplitude chatter data. The proposed work compares vibration signal captured using accelerometer and microphone at different tilt angles for radial milling of thin-walled Ti6Al4V at different radial immersion to analyse the effect of tilt-angle on chatter identification. The recorded signals from both accelerometer and microphone are analysed in time domain by estimation and comparison of statistical parameters, in frequency domain by analysing the distributed frequency and in time–frequency domain by analysing the energy of the signals. Complete characterization of the signals in time, frequency and time–frequency domain/methods indicated that microphone at 60° tilt angle can be used as an alternative sensor for chatter onset identification without loss in chatter data irrespective of cutting condition. Machined surface analysis of thin-walled structures complements the microphone as an effective chatter identification sensor.

Development of automated impact system for modal analysis of micro-end mill

The micro cutting tool used in micromilling process is highly susceptible to instability attributed to its low strength. A catastrophic failure of micro-end mill can occur if the chatter is not controlled during micromilling process, especially for micromilling of difficult-to-machine materials, like Ti and Ni alloys. Consequently, the control of chatter in micromilling process is necessary to manufacture the components with required dimensional and geometrical accuracy. Chatter can be avoided by selecting the stable process parameters from stability lobe diagram or by changing the process parameters during machining process by chatter onset identification. The both ways of chatter avoidance require the reliable identification of the modal parameters of the micro-end mill. Experimental modal analysis is most accurate method for identification of modal parameters owing to its ability of consideration of machine tool compliance. Hence, in the present work, automated impact system has been developed to impact the micro-end mill under both stationary and rotating conditions. The developed impact system has been found to be impacting the micro-end with maximum deviation of 2.6% in force amplitude due to repeated impacts. A maximum variation of 1.7% has been found in the natural frequency due to different impacts of the micro-end mill under both stationary and rotating conditions. The accurate estimation of frequency response function is expected to predict the stability of micromilling process with high reliability.

Case study of vibration in a mobile milling machine

Many industries rely on heavy-duty machinery such as cranes, pipelines, motors, which are subject to wear and tear. Milling of such parts using conventional machine tools presents many drawbacks and is impossible in some cases. In such situations, mobile machine tools can be used to machine directly on site. However, mobile machines are flexible, and are prone to detrimental vibrations during cutting operations. Vibrational stability in the surfacing process has been extensively studied in conventional milling. However, the focus of this article is an original study on a mobile milling machine used for on-site surfacing operations. An experimental investigation into the vibrational behavior of the machine is carried out and a sensorless method is used to detect the onset of chatter. Machine flexibility is discussed using a tool-point frequency response function and references from the literature. Milling operations are carried out over a range machining parameter to determine an experimental stability limit. Internal sensors for spindle motor consumption and spindle motor encoder are monitored during the machining process. A threshold is established for determining the appearance of chatter using only the machine’s internal sensors.

Modeling and Stability Analysis of Robot-assisted Thin-walled Milling

Thin-walled machining has played an important role in automotive and aerospace domains. Due to physical characteristics of the flexible structure, the thin-walled machining is prone to the deformation and the onset of chatter. To solve the problem, a robot is considered to support the flexible workpiece while cutting. This paper studies finite element dynamic modeling and stability analysis of the robot-assisted thin-walled milling system. It is divided into three submodels: thin-walled workpiece, robot, and cutting process. The finite element method (FEM) is adopted to obtain a realistic dynamic model of the workpiece and allows analysis of any location. The robot dynamics is linearized to a 1-degree-of-freedom (DOF) mass-spring-damper system for model reduction. The cutting force variation and the regenerative effects involved in the cutting process establish time-periodic and time delay terms in the milling model. The semi-discretization method (SDM) is used for the stability analysis. Simulation results show a significant improvement in the milling stability from the robot support in any location, leading to higher productivity.

Machined Surface Image Analysis for Process Instability Identification in Micromilling of Ti6Al4V

The low flexural rigidity of micro-end mill used in micromilling process undergoes catastrophic failure at low amplitude chatter and hence it is critical to identify the low amplitude chatter to produce the chatter free machined surfaces. An unfixing of the workpiece to identify the chatter by analyzing the power spectral density of machined surface topography alters the process set-up and even a minute change in workpiece surface flatness during re-fixing the workpiece lead to variation in depth of cut, which changes the precision of machined surface in micromilling process. Consequently, in the present work, an image analysis method has been proposed for machined surface analysis in micromilling of Ti6Al4V. Different statistical parameters like arithmetic gray level average value of the distribution of pixel intensities and optical parameter have been analyzed to detect the process instability. The estimated statistical parameters of the machined surface images for different process parameters has been compared with the measured surface roughness of the machined surfaces. The frequency spectrum analysis of the machined surface image analysis has also been carried out, which shows the distribution of pixel intensities at different frequencies. Arithmetic gray level average of pixel intensity of the machined surface image has been found to be decreasing with an increases of the roughness of the machined surface. The optical parameter of the machined surface increases with the increases of the roughness. The critical value of arithmetic gray level average of pixel intensity and optical parameter was found to be 148 and 0.36, respectively for chatter onset. The distribution of pixel intensity at different frequencies shows the diffused pattern of brightness of the pixel away from the center location in chatter dominant machining while the distribution of brightness of pixel was found to be circular in nature around the central spot in stable machining. The chatter and stable machining condition has also been verified with the vibration spectrum of the workpiece, where the vibration of the workpiece has been captured using accelerometer. The proposed image analysis method can further be used for online monitoring of machining process.

The Effect of Speed-Varying Micro-Cutting Tool Dynamics on Stability During High-Speed Micromilling of Ti6Al4V

Abstract Chatter-free machining is necessary in micromilling to avoid the catastrophic failure of micro-end mill. The accuracy of the prediction of chatter-free machining conditions in high-speed micromilling has been improved in this work by including speed-varying micro-end mill dynamics. An optimum design of exponential window has been devised to remove the unwanted spindle dynamics from the displacement signal to construct the speed-dependent frequency response function (FRF) of micro-end mill. The stiffness of the micro-end mill has been found to be increasing with increase in spindle speed and the natural frequency of the micro-end mill has been found to be changing with change in spindle speeds. The cutting velocity-chip load-dependent cutting coefficients have been included to predict the stability using Nyquist criterion. The predicted stability lobe with speed-varying micro-end mill dynamics has increased chatter-free depth of cut significantly compared to the chatter-free depth of cut predicted with static micro-end mill dynamics. The increase in depth of cut with speed-varying dynamics has been found to be 28% at 20,000 rpm, 150% at 52,000 rpm, and 250% at 70,000 rpm. A critical value of acceleration of the workpiece has been identified for chatter onset detection and it has been validated with machined surface image analysis. The magnitude of acceleration in both feed and normal to feed direction has been characterized to analyze the effect of spindle speed and depth of cut on the vibration of workpiece.

Improvement of boring operations by means of mode coupling effect

Boring bars are inherently slender tools which are prone to show chatter problems due to their low dynamic stiffness and damping, being this problem their main limitation in productivity. The onset of chatter is mainly related to the dynamic stiffness of the bending mode of the boring bar when the length L to diameter D ratio is higher than 4. Tuned mass dampers (TMD) are effective technical solutions to increase the dynamic stiffness of large ratio boring bars. However, there are many applications where 4–6 L / D ratio tools are required, and the avoidance of chatter without the application of TMDs is interesting due to the high cost of damped tools. This work proposes the use of mode coupling effect to increase the damping and stabilise the machining process avoiding the use of any special device. This effect occurs when the frequency of one of the machine’s modes is similar to the frequency of the dominant mode of the boring bar. As a result, the shape of the critical mode of the boring bar is mixed with the mode originated in the machine, and the damping and stability will be higher than the one that is not subjected to any dynamic coupling. The main contribution of this work is the application of this concept to increase stability in boring operations. This objective has been achieved by optimising the tool length and material by means of a dynamic model based on Receptance Coupling Substructure Analysis (RCSA). The model combines an analytical model of the elastic body of the boring bar with the experimental characterisation of the effect of the rest of the machine. This way, the shape and materials of the boring bar can be optimised to create an increase of damping. The optimisation procedure has been experimentally validated resulting in an increase of cutting stability and demonstrating that not always a shorter bar supposes a higher stability.

Early chatter detection in thin-walled workpiece milling process based on multi-synchrosqueezing transform and feature selection

The onset of chatter is extremely disadvantageous to machining process, which will induce poor surface quality, low processing efficiency and severe tool wear, especially in the milling of weakly rigid thin-walled workpieces, so it should be detected as early as possible to avoid further damage. To handle this issue, a multi-synchrosqueezing transform (MSST) based early chatter detection method as well as a novel feature selection algorithm is proposed in this paper to monitor the occurrence of chatter in thin-walled parts machining. MSST is firstly conducted on the sampled vibration signal to generate a time–frequency (TF) representation. The periodic components associated with spindle rotation are removed from the TF matrix and the chatter-related signal is recovered by multi-ridge detection. Then, features in time and frequency domains are extracted from the reconstructed chatter signal. A feature selection algorithm combining multi-feature distance evaluation technique (MFDET) and distance correlation analysis is subsequently presented to determine the optimal feature subset. Compared with other chatter indicators given in existing works, the selected compact feature set is more discriminative and has low redundancy. Furthermore, random forest (RF) is utilized to intelligently diagnose the machining stability. Several experiments under constant and variable conditions are performed to evaluate the performance of the proposed method in terms of early chatter detection. The experimental results demonstrate that the presented chatter monitoring method is highly efficient and robust, which can recognize the chatter onset timely and accurately for thin-walled parts milling under different machining conditions.

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.

Stability analysis in milling process using spline based local mean decomposition (SBLMD) technique and statistical indicators

In the present work, a novel online monitoring system has been developed using a new modified spline based local mean decomposition technique for the identification of tool chatter onset pertaining to the three input milling parameters viz. feed rate, axial depth of cut and spindle speed. Acquired sound signals of machining at various cutting parameters are processed using a Spline Based Local Mean Decomposition (SBLMD) technique. A series of product functions are extracted from the processed signal. Prominent PF’s are combined to reconstruct the signal representing chatter. Reconstructed chatter signal has been analysed to extract tool chatter features. Finally, online monitoring of tool chatter is executed considering the ascertained chatter severity threshold. Viability of the proposed methodology is validated by performing more experiments and the results indicated that it can be well adopted for the online monitoring of tool chatter.

Why is it hard to identify the onset of chatter? A stochastic resonance perspective

A stochastic dynamical model is presented to identify the difficulties in chatter detection during cutting processes. The theoretical implications are based on measurements related to the stochastic character of the cutting force. The stochastic model is validated in a Hardware-In-the-Loop (HIL) environment where the multiplicative component of the stochastic cutting force is varied parametrically. In case of an industrial machine tool, the stochastic resonance effect is also demonstrated quantitatively by means of high-resolution vibration measurements for various spindle speeds in full immersion milling. The proposed method predicts the noise induced peaks in the spectrum of the vibration signals, which occur already within the chatter-free parameter domains and might be misjudged as chatter.

Vibration signature based stability studies in internal turning with a wavelet denoising preprocessor

Detection of accurate chatter is one of the significant problems in machining science due to the contaminated noise present in the acquired signals, which reduces the efficiency of the signal processing tool. In the present work, an efficient chatter detection approach based on the combined modified wavelet de-noising (WD) and Hilbert-Huang transforms (HHT) is adopted. The proposed methodology is implemented in four stages: (i) de-noising of contaminated signals using novel adaptive threshold-based wavelet de-noising, (ii) analysis of de-noised signals with Hilbert-Huang transform to obtain the time-frequency spectrum, (iii) extraction of features such as mean, standard deviation, root mean square and kurtosis of the sensitive intrinsic mode function (IMF) to identify the chatter onset and (iv) classification of chatter onset states based on statistical measures using an improved probabilistic neural network (PNN). The proposed approach is implemented for identifying the cutting states of experimentally measured vibration signals from internal turning operation.

Chatter detection in milling processes using frequency-domain Rényi entropy

Chatter is a kind of self-excited vibration and causes negative effects in machining processes. This paper presents a practical method to identify the chatter with cutting force signals in milling processes. Since the spectrum of the chatter signal exhibits discrete spectral lines around the chatter frequencies and the Renyi entropy is an effective index to characterize the randomness of data series, the frequency-domain Renyi entropy is proposed as a chatter indicator. As the chatter severity level grows, the signal components at the chatter frequencies become more and more significant, which means a reduction of the randomness of the spectral series. As a result, the value of the Renyi entropy-based indicator decreases rapidly at the onset of the chatter. In order to eliminate the interference of the normal signal components, i.e., the spindle speed-related frequency components, the spectrum is preprocessed to filter out those components first. Various milling experiments are conducted. The results show that the value of the proposed indicator changes sharply at the onset of chatter in various milling conditions with different spindle speeds and cutting depths. Also, the proposed indicator is compared with the commonly used Shannon entropy-based indicator and verified to have a larger difference between the stable and chatter statuses and is higher sensitivity to the chatter.

A novel online chatter detection method in milling process based on multiscale entropy and gradient tree boosting

Abstract In the milling process, chatter, which results in poor surface quality, dimensional errors, reduced cutter and machine life, is one of the main limitations on performance. In this paper, a novel method of online chatter detection for milling processes is developed. In this method, first, the spindle revolution period component is obtained via angular synchronous averaging (ASA). Then, the residual part related to chatter information is calculated by subtracting the periodic component. Secondly, the multiscale permutation entropy (MPE) and multiscale power spectral entropy (MPSE) of the residual part are calculated, and the Laplacian score (LS) for feature selection is applied to select the optimal sensitive scale features with generalization. Online chatter detection that is based on selected sensitive scale features by splitting signal up into (overlapping) frames in milling process. Finally, a trained gradient tree boosting (GTB) can be used to intelligent diagnosis of the chatter severity level. The analysis results show that the proposed method can effectively detect the onset of chatter under stable cutting conditions and variable cutting conditions, which is simpler and more robust than the existing methods.

A Dedicated Design Strategy for Active Boring Bar

Unstable vibrations (i.e., chatter) onset is one of the main limits to productivity in deep boring bar processes. Active damping systems allow to increase machining stability in different configurations (i.e., tool setup), without requiring cutting system dynamic characterization. Design of an active boring bar involves the development of monitoring system (sensors), actuation system and control logic. While several control logics were evaluated and discussed, few design solutions were presented in the literature, focusing only on building prototypes to demonstrate control logic effectiveness. In the presented work, a deep analysis of the main issues and requirements related to active boring design was carried out and a systematic approach to tackle all the critical aspects was developed. The results of the proposed method are: (i) optimal actuators positioning able to damp vibration along two directions; (ii) preload system design guaranteeing the correct actuator preloading for the operating conditions; (iii) covers design to protect actuators and ensure the dynamic and static equivalence between active and standard boring bar. Following this approach, an active boring bar was designed, realized and tested. The results prove the required equivalence between active and original boring bar and assess the damping effect.

Dynamic modeling and chatter analysis of a spindle-workpiece-tailstock system for the turning of flexible parts

Chatter happens frequently in turning of flexible parts, jeopardizing the dynamic stability of the operation. The chatter stability prediction is dependent on the accurate estimation of the structural parameters of the cutting system. This paper aims to quantify the influence of the support conditions on dynamics of the flexible parts in straight turning. Given the time-varying cutting tool position during machining, first, the chatter stability of the cutting system along the cutting path is analyzed, and the critical stiffness expression for chatter onset prediction is deduced. Then, two models of the spindle-workpiece-tailstock system using analytical and numerical methods are constructed and compared. The simulation results showed that increasing the spindle bearing or tailstock support stiffness cannot effectively improve the stiffness distribution and the eigenfrequency of the machined rod, whereas the frequency of the system tends to be increased evidently with the chuck size decreased. In comparison with the experimental results, it has been shown that the model involving the effect of spindle bearing and center support enables to produce more accurate modal parameters estimation and chatter onset location prediction than the simplified model. Besides, the measured displacement signals demonstrate a good mapping relation with the theoretical analysis results. The present study could provide an in-depth understanding of the dynamic behavior of flexible workpieces in turning operations.

Real-time chatter detection and automatic suppression for intelligent spindles based on wavelet packet energy entropy and local outlier factor algorithm

In this research, we proposed a real-time chatter detection and suppression module for intelligent spindle to increase machining efficiency and processing yield. For early detections of chatters, the relative wavelet packet energy entropy with high sensitivity in the high-frequency band and the local outlier factor (LOF) algorithm were utilized as chatter features and classifications, respectively. Based on the pre-obtained three-dimensional stability lobe diagram (SLD) and a LOF-based trained model, the module could real-time monitor and suppress chatter during machining processes. The module was implemented and experimentally examined with the CNC end-milling machine under five different cutting conditions for verifying the capabilities of real-time chatter identifications and suppressions. It was demonstrated that the module could detect the onset of chatter and suppress it by changing the cutting conditions to avoid damages on the surfaces of working piece due to severe chatters.

An Integro-Differential Equation with Variable Delay Arising in Machine Tool Vibration

We rigorously derive an integro-differential equation as a model for the possible onset of regenerative chatter during a turning process using a lathe. The cut is made parallel to the axis of rotation of the spindle. The model allows the spindle speed to continuously vary with time, which results in the presence of a variable time delay determined from a threshold condition. We present a number of conditions sufficient for the elimination of chatter; these emphasize sufficiently low feed rate or sufficiently high spindle speed. Numerical simulations cast light on the effect of a sinusoidally varying spindle speed, a feature of some modern lathes. Spindle speed variation can cure chatter but does not necessarily do so and can fail at higher values of the tool feed rate.

Chatter Suppression in Robotic Milling by Control of Configuration Dependent Dynamics

Serial industrial robots tend to have low static stiffness and possess configuration dependent dynamics. The former results in an earlier onset of chatter compared to machine tools whereas the latter introduces robot configuration dependency into the process stability. In this paper, the robot configuration is continuously varied around its functional redundancy to create time varying system dynamics to suppress and/or avoid chatter in a robotic milling operation. It was shown that, at low spindle speeds it is possible to suppress chatter even though the quasi-static stability predictions may indicate instability at different robot configurations for the chosen process parameters.