Figure - available from: Measurement Science and Technology
This content is subject to copyright. Terms and conditions apply.
Source publication
Complex and non-stationary cutting chatter affects productivity and quality in the milling process. Developing an effective condition-monitoring approach is critical to accurately identify cutting chatter. In this paper, an integrated condition-monitoring method is proposed,
where reduced features are used to efficiently recognize and classify mach...
Similar publications
The calculation of temporally varying upstream process outcomes is a challenging task. Over the last years, several parametric, semi-parametric as well as non-parametric approaches were developed to provide reliable estimates for key process parameters. We present generic and product-specific recurrent neural network (RNN) models for the computatio...
Air pollution is a common problem for many countries around the world in the process of industrialization as well as a challenge to sustainable development. This paper has selected Chengdu-Chongqing region of China as the research object, which suffers from severe air pollution and has been actively involved in air pollution control in recent years...
We present a novel training method for deep operator networks (DeepONets), one of the most popular neural network models for operators. DeepONets are constructed by two sub-networks, namely the branch and trunk networks. Typically, the two sub-networks are trained simultaneously, which amounts to solving a complex optimization problem in a high dim...
Label information plays an important role in supervised high-resolution remote sensing (HRRS) image scene classification. However, the labels of a dataset are probably unreliable and may contain “noisy” labels. Focusing on uncertain labels problem, a covariance matrix representation-based noisy label model (CMR-NLD) is designed for HRRS image scene...
Citations
... parameters of time-frequency statistics and probabilistic principal component analysis. Liu et al. [15] proposed that the milling process and chatter due to cutting affect productivity and quality. To monitor the occurrence of chatter effectively, the variational mode decomposition method was used to break down the vibration signal into several signals, and the PCA method was used to reduce the viewing dimension. ...
To improve stability and accuracy during machining, the structural performance of machine tools must be predicted in advance. This study aims to improve the performance of a large-scale turning-milling complex machine tool by analyzing its structural characteristics and health status. Modal and spatial accuracies of an actual machine tool were analyzed using the finite element method based on the working environment and material properties. Static analysis was used to determine spatial processing position deformation errors and an appropriate compensation value to improve accuracy. The modal analysis determined the characteristic parameters of the main machine structure in a fair frequency range, and the modal analysis software verified the machine frequency using modal shape curve fitting. Modal error percentages between the virtual and machine models determined the validation of the model. The prediction diagnosis performance system monitored the spindle vibration signal to evaluate its health status. However, the use and maintenance of each piece of equipment differed. Abnormal symptoms of the spindle are observed in specific frequency bands or vibration characteristics. The feature modeling can establish a health diagnosis model and use the principal component analysis method to observe vibration characteristics and identify machine health.
... As to feature dimension reduction, principal component analysis (PCA) is a commonly utilized technique, as shown in [175,186,196]. Fu et al. [132], Chen et al. [144] and Dun et al. [185] employed it as a reference to show the advantages of their methods. ...
... Fu et al. [132], Chen et al. [144] and Dun et al. [185] employed it as a reference to show the advantages of their methods. Jo et al. [253] suggested that the use of the modified independent component analysis (MICA) method outperforms PCA, while Liu et al. [175] illustrated the contribution of PCA with different signal processing and classification methods. ...
... Approximate entropy [88,133,169,170,183] Sample entropy and others [133,141,142,162,171,186,198,223] Power spectral entropy [90,111,121,134,138,157,175,176] Permutation entropy [134,157] Complexity and hand-crafted [76, 78, 80, 83, 84, 90, 115, 118, 120, 121, 123, 126, 128, 129, 132, 144, 146, 147, 149, 150, 163-165, 169, 174, 177, 179, 180, 184, 193, 197, 202, 204, 217, 220, 227, 239, 244, 255, 259, 326, 353] showed that the accuracy of each model varies according to the utilized feature, and SVM had the highest susceptibility compared to the other methods, while the probabilistic neural network (PNN) had a slightly lower accuracy. The performance of NNs is similar to SVM, and it has some advantages such as lower training time [145,180]. ...
Among the diverse challenges in machining processes, chatter has a significant detrimental effect on surface quality and tool life, and it is a major limiting factor in achieving a higher material removal rate. Early detection of chatter occurrence is considered a key element in the milling process automation.
Online detection of chatter onset has been continually investigated over several decades, along with the development of new signal processing and machining condition classification approaches. This paper presents a review of the literature on chatter detection in milling, providing a comprehensive analysis of the reported methods for sensing and testing parameter design, signal processing and various features proposed as chatter indicators. It discusses data-driven approaches, including the use of different techniques in the time–frequency domain, feature extraction, and machining condition classification.
The review outlines the potential of using multiple sensors and information fusion with machine learning. To conclude, research trends, challenges and future perspectives are presented, with the recommendation to study the tool wear effects, and chatter detection at dissimilar milling conditions, while utilization of considerable large datasets—Big Data—under the Industry 4.0 framework and the development of machining Digital Twin capable of real-time chatter detection are considered as key enabling technologies for intelligent manufacturing
... VMD, an adaptive signal machining method, is conducive to the extraction of chatter features, effectively dealing with the characteristic that chatter frequency bands shift during machining. Liu et al. (2017) used the chatter of milling on an NC machine as the analysis object, extracted sample features by VMD and Shannon power spectral entropy, and classified and predicted samples using a probabilistic neural network (PNN). However, the parameters that affect the accuracy of the model are selected based on prior knowledge, without further optimization processes. ...
... found by alternately updating u n+1 k , n+1 k , and n+1 , obtaining k signal sub-sequences (Liu et al. 2017). ...
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.
... The initial stage in this approach is to attain the signal. Researchers have employed different sensors to acquire signal-like force sensors [3], acceleration sensors [4][5][6], audio sensors [7,8] and motor current [9,10]. Delio et al compared acceleration sensors, displacement sensors with audio signals, and deduced that chatter detection using audio signals is more effective and less costly [7]. ...
... To overcome such issues, researchers have proposed various novel self-adaptive techniques such as empirical mode decomposition (EMD) [16], ensemble empirical mode decomposition (EEMD) [17], empirical wavelet transform (EWT) [18,19] and variational mode decomposition (VMD) [5,20]. However, all these self-adaptive signal processing techniques have their limitations, such as EMD and EEMD being known for limitations like the end effect, mode aliasing, sensitivity to noise and sampling [21]. ...
In recent decades, lots of work has been done to mitigate self excited vibration effects in milling operations. Still, a robust methodology is yet to be developed that can suggest stability bounds pertaining to higher metal removal rate (MRR). In the present work, experimentally acquired acoustic signals in milling operation have been computed using a modified Local Mean Decomposition (SBLMD) technique in order to cite tool chatter features. Further, three artificial neural network (ANN) training algorithms viz. Resilient Propagation (RP), Conjugate Gradient-Based (CGP) and Levenberg-Marquardt Algorithm (LM) and two activation functions viz. Hyperbolic Tangent Sigmoid (TANSIG) and Log Sigmoid (LOGSIG) has been used to train the acquired chatter vibration and metal removal rate data set. Over-fitting or under-fitting issues may arise from the random selection of a number of hidden neurons. The solution to these problems is also proposed in this paper. Among these training algorithms and activation functions, a suitable one has been selected and further invoked to develop prediction models of chatter severity and metal removal rate. Finally, Multi-Objective Particle Swarm Optimization (MOPSO) has been invoked to optimize developed prediction models for obtaining the most favourable range of input parameters pertaining to stable milling with higher productivity.
... However, these traditional models still have to face some problems [26][27][28][29]. On the one hand, feature extraction and selection are essential but labor intensive, and require both sufficient domain expertise and rich experience [1,28,30]. ...
A reliable data-driven tool condition monitoring system is more and more promising for cutting down on machine downtime and economic losses. However, traditional methods are not able to address machining big data because of low model generalizability and laborious feature extraction by hand. In this paper, a novel deep learning model, named multi-frequency-band deep convolution neural network (MFB-DCNN), is proposed to handle machining big data and to monitor tool condition. First, samples are enlarged and a three-layer wavelet package decomposition is applied to obtain wavelet coefficients in different frequency bands. Then, the multi-frequency-band feature extraction structure based on a deep convolution neural network structure is introduced and utilized for sensitive feature extraction from these coefficients. The extracted features are fed into full connection layers to predict tool wear conditions. After this, milling experiments are conducted for signal acquisition and model construction. A series of hyperparameter selection experiments is designed for optimization of the proposed MFB-DCNN model. Finally, the prediction performance of typical models is evaluated and compared with that of the proposed model. The results show that the proposed model has outstanding generalizability and higher prediction performance, and a well designed structure can remedy the absence of complicated feature engineering.
... The other is based on a data-driven method, which relies on a large amount of historical data (these data are selected or calculated from the historical values of parameters which can be collected from aluminum electrolytic cells) to train models, instead of building models based on the fault mech anisms [16][17][18][19][20]. In early studies, much work related to AE was based on principal component analysis (PCA) [21][22][23] or multivariate statistical methods [1,24]. ...
The anode effect (AE) often occurs in the aluminum electrolysis process, which seriously affects production efficiency and causes large energy consumption. Therefore, predicting AE in advance has become very important. However, traditional cell resistance-based methods have the disadvantages of low accuracy and short predicting time, and the data-driven methods have not fully considered the relevance between other features and AE, causing the low accuracy for long-term prediction. In this paper, a long-term prediction approach for AE based on a modified neighborhood mutual information and light gradient boosting machine (MNMI-LGBM) is proposed. The MNMI is used to measure the relevance between features and AE for feature selection, and a derivative feature of the cell resistance is fed into the LGBM as a key feature to predict AE. The predicting time in the range of 0–50 min is considered. The results show that the proposed approach has an accuracy above 89% over the entire 0–50 min predicting time range. Specifically, the accuracy achieves 93.6% when predicting time is 50 min. The proposed approach only requires six features as the input of LGBM and can be applied for real-time AE prediction.
... The key aspects for vibration tendency prediction are signal processing and fitting regression [4]. The essential information that reflects the operating states under the environment of noise and electromagnetic interference can be obtained by signal processing [5][6][7]. Then, the vibration tendency of an HGU can be derived by generalized regression. ...
The hydropower generator unit (HGU) is a vital piece of equipment for frequency and peaking modulation in the power grid. Its vibration signal contains a wealth of information and status characteristics. Therefore, it is important to predict the vibration tendency of HGUs using collected real-time data, and achieve predictive maintenance as well. In previous studies, most prediction methods have only focused on enhancing the stability or accuracy. However, it is insufficient to consider only one criterion (stability or accuracy) in vibration tendency prediction. In this paper, an intelligence vibration tendency prediction method is proposed to simultaneously achieve strong stability and high accuracy, where vibration signal preprocessing, feature selection and prediction methods are integrated in a multi-objective optimization framework. Firstly, raw sensor signals are decomposed into several modes by empirical wavelet transform (EWT). Subsequently, the refactored modes can be obtained by the sample entropy-based reconstruction strategy. Then, important input features are selected using the Gram-Schmidt orthogonal (GSO) process. Later, the refactored modes are predicted through kernel extreme learning machine (KELM). Finally, the parameters of GSO and KELM are synchronously optimized by the multi-objective salp swarm algorithm. A case study and analysis of the mixed-flow HGU data in China was conducted, and the results show that the proposed model performs better in terms of predicting stability and accuracy.
... However, two parameters of VMD, i.e. the mode number k and update parameter τ , need to be preset. [15,16]. ...
... In addition, each mode u k is concentrated around the center frequency ω k (k = 1, 2, . . . , K) [15]. ...
Due to the non-stationary and nonlinear characteristics of rotating machinery fault signals, it is difficult to identify different fault conditions using only traditional time-frequency domain analysis approaches. In this paper, a combined method based on improved variational mode decomposition (IVMD) and a hybrid artificial sheep algorithm (HASA) is proposed for rotating machinery fault diagnosis. In the proposed method, the IVMD is used to decompose the signal into several modes, which can determine the mode number k and update parameter adaptively. Then, the HASA is utilized for feature selection and parameter optimization, where the binary-valued artificial sheep algorithm (BASA) is employed to select the optimal features and the real-valued artificial sheep algorithm (RASA) is used to optimize the parameters of a support vector machine (SVM). Moreover, the HASA effectively extends the optimization range of the decision variables by the compound use of the BASA and RASA. Two experimental cases, including rolling bearing faults and rotor system faults, have been implemented to test the performance of the proposed diagnosis method. The experimental results demonstrate that the proposed method can achieve better classification accuracies in practical applications.
... An alternative to the traditional feature analysis-based anode effect prediction approaches is the data-driven method. This method just relies on a lot of historical data without analyzing the detailed mechanism [12][13][14][15][16]. Currently, a lot of works based on data-driven methods are being applied to anode effect prediction. ...
An anode effect often occurs during the process of aluminum electrolysis that will cause large energy consumption and low efficiency in aluminum production, thus how to identify the anode effect in advance has become an important issue. However, traditional approaches ignore the common incomplete information problem existing in the acquired data, and only consider a single predicting time, resulting in an unreliable result in anode effect prediction. In this paper, a hybrid prediction approach based on a singular value thresholding and extreme gradient boosting (SVT-XGBoost) approach is proposed to identify the anode effect in the aluminum electrolysis process. The SVT is used for data filling by the whole-features transformation, and the XGBoost is utilized for classification of the anode effect. The predicting time is set to 10 min by the comparison. The experimental results show that the proposed approach has an effective ability for anode effect classification using the SVT-XGBoost compared to the previous approaches. Here, the effect of the training sample number is also investigated. The proposed approach could be applied in real-time anode effect prediction in the future.
... As a comparison of the proposed unsupervised classification approach, some traditional supervised classification methods, including hidden Markov model (HMM) [35], support vector machines (SVM) [36], genetic algorithm-based back propagation neural network (BPNN-GA) model [37], and probabilistic neural network (PNN) model [38], are also applied to classify the reduced features from five different machine states. ...
The reliability and performance of additive manufacturing (AM) machines affect the product quality and manufacturing cost. Developing effective health monitoring and prognostics methods is critical to AM productivity. Yet limited work is done on machine health monitoring. Recently, the application of acoustic emission sensor (AE) to the fault diagnosis of material extrusion or fused deposition modeling process was demonstrated. One challenge in real-time process monitoring is processing the large amount of data collected by high-fidelity sensors for diagnostics and prognostics. In this paper, the efficiency of machine state identification from AE data is significantly improved with reduced feature space dimension. In the proposed method, features extracted in both time and frequency domains are combined and then reduced with the linear discriminant analysis. An unsupervised density based clustering method is applied to classify and recognize different machine states of the extruder. Experimental results show that the proposed approach can effectively identify machine states of the extruder even within a much smaller feature space.
