Chatter detection in metal machining is important to ensure good surface quality and avoid damage to the machine tool and workpiece. This paper presents an intelligent chatter detection method in a multi-channel monitoring system comprising vibration signals in three orthogonal directions. The method comprises three main steps: signal processing, feature extraction and selection, and classification. The ensemble empirical mode decomposition (EEMD) is used to decompose the raw signals into a set of intrinsic mode functions (IMFs) that represent different frequency bands. Features extracted from IMFs are ranked using the Fisher discriminant ratio (FDR) to identify the informative IMFs, and those features with higher FDRs are selected and presented to a support vector machine for classification. Single-channel strategies and multi-channel strategies are compared in low immersion milling of titanium alloy Ti6Al4V. The results demonstrate that the

Chatter is a self-excited vibration that affects the part quality and tool life in the machining process. This paper introduces an intelligent chatter detection method based on image features and the support vector machine. In order to reduce the background noise and highlight chatter characteristics, the average FFT is applied to identify the dominant frequency bands that divide the time-frequency image of the short-time Fourier transform into several sub-images. The non-stationary properties of the machining conditions are quantified using sub-images features. The area under the receiver operating characteristics curve ranks the extracted image features according to their separability capabilities. The support vector machine is designed to automatically classify the machining conditions and select the best feature subset based on the ranked features. The proposed method is verified by using dry micro-milling tests of steel 1040 and high classification accuracies for both the stable and unstable tests are obtained. In addition, the proposed method is compared with two additional methods using either image features from the continuous wavelet transform or time-domain features. The results present a better classification performance than the two additional methods, indicating the efficiency of the proposed method for chatter detection.

Chatter in machining results in poor workpiece surface quality and short tool life. A reliable chatter detection method is needed to monitor this self-excited vibration before its complete development. This paper applies multifractal features extracted from a novel p-leader multifractal formalism for chatter detection in milling processes. This novel formalism is able to discover a complex singular behavior rising on unstable chatter signals, and improves chatter detection performance. The multifractal features are selected using a multivariate filter, and assessed in terms of their dynamic monitoring abilities and classification accuracies under wide cutting conditions. The results show that the multifractal features cannot only detect chatter with high accuracy and short computation time, but are also efficient to distinguish stable tests with large amplitudes from unstable tests. A small p is recommended to highlight dynamic variations and obtain high classification accuracies. For further verification, the

Chatter is a cause of low surface quality and productivity in milling and crucial features need to be extracted for accurate chatter detection and suppression. This paper introduces a novel feature extraction approach for chatter detection by using image analysis of dominant frequency bands from the short-time Fourier transform (STFT) spectrograms. In order to remove the environmental noises and highlight chatter related characteristics, dominant frequency bands with high energy are identified by applying the squared energy operator to the synthesized fast Fourier transform (FFT) spectrum. The time-frequency spectrogram of the vibration signal is divided into a set of grayscale sub-images according to the dominant frequency bands. Statistical image features are extracted from those sub-images to describe the machining condition and assessed in terms of their separability capabilities. The proposed feature extraction method is verified by using dry milling tests of titanium alloy Ti6Al4V and compared with two existing feature extraction techniques. The results indicate the efficiency of the time-frequency image features from dominant frequency bands for chatter detection and their better performance than the time domain features and wavelet-based features in terms of their separability capabilities.

Electromagnetic radiation and noise pollution are two of the four major environmental pollution sources. Although various materials with excellent microwave absorption performances or sound absorption properties have been manufactured, it is still a great challenge to design materials with both microwave absorption and sound absorption abilities due to different energy consumption mechanisms. Herein, a combination strategy based on structural engineering was proposed to develop bi-functional hierarchical Fe/C hollow microspheres composed of centripetal Fe/C nanosheets. Both of the interconnected channels created by multiple gaps among the adjacent Fe/C nanosheets and the hollow structure have positive effects on the absorbing performances by promoting the penetration of microwaves and acoustic waves and prolonging action time between microwave energy and acoustic energy with materials. In addition, a polymer-protection strategy and a high-temperature reduction process were applied to keep this unique morphology and further improve the performances of the composite. As a result, the optimized hierarchical Fe/C–500 hollow composite exhibits a wide effective absorption bandwidth of 7.52 GHz (10.48–18.00 GHz) at only 1.75 mm. Furthermore, the Fe/C–500 composite can effectively absorb sound wave in the frequency of 1209–3307 Hz, basically including part of the low frequency range (<2000 Hz) and most of the medium frequency range (2000–3500 Hz), and has 90% absorption of sound at 1721–1962 Hz. This work puts new insight into the engineering and development of microwave absorption–sound absorption-integrated functional materials with promising applications.