Intelligent spindles are core components of the next-generation of intelligent/smart machine tools in the Industry 4.0 Era. The purpose of this paper is to clarify the concept of intelligent spindles ...and provide an in-depth review of the state-of-the-art of related technologies. A new integrated concept for intelligent spindles is proposed, followed by descriptions of required characteristics, key enabling technologies and expected intelligent functions. Relevant research that may be beneficial to the development of intelligent spindles is reviewed from six thrust areas, which include monitoring and control of tool condition, chatter, spindle collision, temperature/thermal error, spindle balance, and spindle health. Finally, current limitations and challenges are discussed, and future trends of intelligent spindles are prospected from various perspectives.
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•A new integrated concept for intelligent spindles is proposed.•Previous research works related with intelligent spindles are reviewed.•Future trends of intelligent spindles are prospected.
Chatter detection and suppression/control is an effective way to ensure the quality of workpiece in machining process. In this paper, a new method is proposed to detect early chatter before the ...workpiece surface is deteriorated. As a preparation work, various samples in stable cutting conditions are collected to train the self-organizing map (SOM) neural network. During online chatter monitoring, firstly, vibration signals are measured in real time and multi-features are extracted with signal processing methods to form a feature vector. Then, the feature vector is input to the trained SOM neural network. The minimum quantization error (MQE), namely, the Euclidean distance between the best matching unit (BMU) of the SOM neural network and the feature vector, is calculated as a new chatter detection indicator. Lastly, the MQE is compared with a 3
σ
criterion-based threshold, which is independent of cutting conditions, to determine the chatter occurrence. The proposed method was verified with end milling tests, and the results showed that the chatter can be detected before serious marks were left on the workpiece surface.
Chatter detection is an important task to improve productivity and part quality in the machining process. Since measured signals from sensors are usually contaminated by background noise and other ...disturbances, it is necessary to find efficient signal processing algorithms to identify the chatter as soon as possible. This paper presents an effective chatter identification method for the end milling process based on the study of two advanced signal processing techniques, i.e., wavelet package transform (WPT) and Hilbert–Huang transform (HHT). The WPT works as a preprocessor to denoise the measured signals and hence the performance of the HHT is enhanced. The proposed method consists of four steps. First, the measured signals are decomposed by the WPT, so that the chatter signals are allocated in a certain frequency band. Secondly, wavelet packets with rich chatter information are selected and are used to reconstruct new signals. Thirdly, the reconstructed signals are analyzed with HHT to obtain a Hilbert–Huang spectrum, which is a full time–frequency–energy distribution of the signals. Finally, the mean value and standard deviation of the Hilbert–Huang spectrum are calculated to detect the chatter and identify its levels as well. The proposed method is applied to the end milling process and the experimental results prove that the method can identify the chatter effectively.
► An effective chatter identification method is presented for end milling process. ► HHT with the WPT preprocessor works more efficiently than individual HHT. ► The mean value and standard deviation of the Hilbert–Huang spectrum are calculated as chatter indices.
Chatter is a self-excited vibration between the workpiece and tool with negative effects. In this work, a chatter detection method is proposed based on synchrosqueezing transform (SST) of sound ...signals. Firstly, the SST is used to analyze the sound signals recorded with the microphone and a time-frequency representation is obtained. Then, filtering is conducted to remove the disturbance of tooth passing frequency and its harmonics in time-frequency domain. Next, singular value decomposition (SVD) method is employed to condense the TF matrix and the first-order singular value is calculated as the chatter indicator. Finally, chatter threshold is set based on 3
σ
criterion for the detection of chatter occurrence. The proposed method is validated with cutting tests, and the results show that the method has great potential to be used for the online chatter detection of high-speed milling process.
The occurrence of stable cage whirl in ball bearings with outer ring guided cage under solid lubrication conditions is investigated based on dynamic simulations. The roles of the cage forces in ...stable whirls and the ball spacing characteristics under stable and unstable conditions are investigated. Results show that the stable whirl motion significantly depends on the ball-cage pocket contact force and the cage-guiding ring forces. Unequal ball spacing is important to the increase of the whirl radius for stable whirl. Furthermore, the ball cannot drive the cage continuously under severe skidding conditions, and thus increase the cage instability.
•Forces to hold constant whirl radius and speed are studied by dynamic simulations.•The constant whirl radius significantly depends on the ball-cage pocket contact force.•Ball-cage pocket contact force is the main driving force for a cage to whirl ahead.•Unequal ball spacing is an important characteristic for stable whirl.•Cage instability occurs under severe skidding conditions.
In cutting process, chatter is an inevitable phenomenon that greatly affects workpiece surface quality, tool life and machining efficiency. The stiffness variation (SV) method has been proposed and ...applied in chatter suppression for a long time. However, the early studies focused on boring and turning with one degree of freedom. For milling process with two degrees of freedom, there is no related research about SV. In this paper, SV method is employed through modulating the stiffness around a nominal value in order to suppress milling chatter. The milling dynamic model of SV with two degrees of freedom is constructed. In this model, the classical delay differential equation (DDE), which governs the milling process, is replaced with a DDE with a time-varying stiffness term. The stability analysis with different SV is completed using the semi-discretization method (SDM) and results show that the stable region with SV is larger than that under most of the traditional conditions. The results of stability analysis are verified by time domain simulation. In addition, the influences on stability lobe diagram (SLD), which is caused by different waveforms, amplitudes and frequencies of SV, are also analyzed specifically. The analysis results can provide the optimal parameters combination for milling. Cutting experiments are implemented on a three-axis milling machine to validate the effectiveness of SV. In the experiment, piezoelectric stack actuators are used to modulate the stiffness with time-varying preload. The milling forces signals are acquired by data collecting instrument, whose root-mean-square (RMS) is used as the metric of cutting vibrations. Experiment results are in good agreement with theoretical prediction.
•The milling dynamic model of stiffness variation with two degrees of freedom is proposed.•The influences of different types of stiffness variation on stability lobe diagram are analyzed.•The model and analysis results are verified by a number of milling tests.
Condition monitoring and fault diagnosis of rolling element bearings timely and accurately is very important to ensure the reliable operation of rotating machinery. In this paper, a multi-fault ...classification model based on the kernel method of support vector machines (SVM) and wavelet frame, wavelet basis were introduced to construct the kernel function of SVM, and wavelet support vector machine (WSVM) is presented. To seek the optimal parameters of WSVM, particle swarm optimization (PSO) is applied to optimize unknown parameters of WSVM. In this work, the vibration signals measured from rolling element bearings are preprocessed using empirical model decomposition (EMD). Moreover, a distance evaluation technique is performed to remove the redundant and irrelevant information and select the salient features for the classification process. Hence, a relatively new hybrid intelligent fault detection and classification method based on EMD, distance evaluation technique and WSVM with PSO is proposed. This method is validated on a rolling element bearing test bench and then applied to the bearing fault diagnosis for electric locomotives. Compared with the commonly used SVM, the WSVM can achieve a greater accuracy. The results indicated that the proposed method can reliably identify different fault patterns of rolling element bearings based on the vibration signals.
► A multi-fault classification model is proposed based on PSO algorithm and WSVM. ► The PSO algorithm is employed to select the parameters of WSVM. ► WSVM classifiers based on different wavelet kernels are presented. ► Classification accuracies are improved using wavelet kernels compared to the normal RBF kernel.
The chatter stability of machine tool is dependent on the dynamic behavior of the spindle system, which is often expressed as the frequency response function (FRF) at the tool tip. The stability lobe ...diagram generated from stationary FRFs can lead to an inaccurate prediction in high and ultra high speed machining. This paper presents an alternative approach to predict the chatter stability lobes of high-speed milling with consideration of speed-varying spindle dynamics. With a dynamic model of a high-speed spindle system, the speed effects (i.e., gyroscopic moment and centrifugal forces) on both the spindle shaft and bearings are investigated systematically with simulations and experiments. The gyroscopic moment of the spindle shaft can increase the cross FRFs, but can hardly affect the direct FRFs at the tool tip due to the damping of the spindle system. The centrifugal forces on both the shaft and bearings lower the overall spindle system stiffness evidently as the speed increases. The speed-dependent FRFs at the tool tip are obtained from the dynamic spindle model and then integrated into the characteristic equation of the dynamic milling system. Nyquist stability criterion is used to generate the chatter stability lobe of high-speed milling operations. It is shown that the stability lobes with speed effects shift to the low speed range significantly. Finally, milling tests are performed to validate predicted the speed-dependent stability lobe.
► An alternative method is presented to predict the speed-dependent chatter stability lobes. ► The gyroscopic moment of the spindle shaft can increase the cross FRFs. ► The gyroscopic moment can hardly affect the direct FRFs due to the damping. ► The centrifugal forces lower the overall spindle system stiffness evidently. ► The stability lobes with speed effects shift to the low speed range significantly.
Chatter is a self-excited vibration between the workpiece and tool. In view of the non-stationarity of the chatter signal, the synchrosqueezing transform (SST) is used to process vibration signals ...during cutting, which can enhance the energy ratio of chatter. In order to eliminate the interference of tooth passing frequency and its harmonics, a time-frequency filtering method is applied to filter these frequency components out. Then, the vibration signal is reconstructed by inverse SST and statistical indexes in time and frequency domains are calculated. The cutting tests are carried out to select statistical indexes which are sensitive to chatter. The effectiveness of the proposed method is verified with cutting tests, and the results show that the chatter can be detected successfully before severe chatter marks are left on the workpiece.
Chatter is a kind of self-excited vibration which frequently occurs in high-speed milling processes, which induces severe damage to both spindle tools and workpieces. In this paper, we introduce a ...new chatter detection technique using ordered-neurons long short-term memory (ON-LSTM) and population based training (PBT). First, we conduct a large number of milling experiments on a computer numerical control (CNC) milling machine with 4 accelerometers to get the dataset and employ vanilla LSTM for chatter detection. Then, to interpret the performance on time series of recurrent neural networks (RNN), a variation of LSTM named ON-LSTM is applied to chatter detection and a hyperparameter tuning method PBT is used for training. Finally, we compare the trained ON-LSTM with the time-frequency spectrum of the original signals obtained by short-time Fourier transform (STFT), and they show a certain degree of consistency.
