Добірка наукової літератури з теми "Early-chatter detection"

It is well known that chatter is one of the main bottlenecks affecting the surface quality of workpieces and production efficiency during machining. In this paper, a new chatter monitoring methodology is proposed on the basis of sparse representation and image similarity measurement to recognise chatter in the manufacturing process. Due to its non-stationary nature with regard to chatter signals, variational mode decomposition (VMD) is utilised and timefrequency entropy (TFE) based on VMD is introduced to measure the complexity. Then, the overcomplete dictionary is pre-trained using the characteristic matrix image extracted from the multi-domain features, thus facilitating the description of chatter from various perspectives. Subsequently, the visualised sparse coefficient matrix is acquired from the trained dictionary and regarded as the reference image, in which detailed information can be obtained from the visualisation of the image. Next, an image similarity measurement method is applied to assess the similarity between the tested sparse coefficient image and the reference image, thereby considering the local and global quality maps such that a comprehensive index for chatter detection can be obtained to fuse the various features. Finally, to validate the proposed methodology, experimental chatter tests are conducted under different machining conditions. The results demonstrate that the chatter can be discriminated at the early stage of chatter development, thus leaving more time to take suppression measures.

Chatter vibration is an undesired and indispensable phenomenon in turning operation, which cannot be completely avoided. However, it can be suppressed by early identification and with the proper choice of input turning parameters. The key issue of chatter detection is to process the acquired signals and extract the features pertaining to it. In the present work, a methodology has been proposed for exploring tool chatter features in the incipient stage during turning on lathe. Chatter signals generated during the turning of Al 6061-T6 have been acquired using a microphone. A stability lobe diagram has been plotted to access the stability regime. Further, in order to study the effect of feed rate on stability, the recorded signals have been processed using a local mean decomposition signal processing technique, followed by the selection of dominating product functions using the Fourier transform. The decomposed signals have been used to evaluate the new output parameter, that is, chatter index. Further, the Nakagami probability distribution has been used to ascertain stability region (threshold). From the experimental validation, it has been inferred that cutting combinations obtained from the Nakagami probability distribution are significant and capable of limiting chatter vibrations. The present methodology will serve as guidelines to the researchers and machinist for the identification of tool chatter in the incipient stage, explore its severity, and finally suppress it with the proper selection of input turning parameters.

242 Background: Twitter provides a platform for health care stakeholders to disseminate information about diseases to patients, caregivers, and doctors. Chats are especially effective because participants can interact directly with experts. Pancreatic cancer (PC) conversations on Twitter previously were sporadic and inconsistent. The authors report the creation of #PancChat, a first-of-its-kind Tweet Chat developed to provide relevant, credible, and timely information to the PC community. A collaboration between leading PC organizations, a pharmaceutical company, and an academic oncologist, PancChat is an example of successful outreach using an accessible communications tool. Methods: Launched in April 2016, the hour-long monthly chat is a live event publicized and promoted through multiple social media channels and major news outlets. It is moderated and focused around a pre-selected topic. The hashtag #PancChat is used to filter specific chatter into a single conversation. Participants include patients, caregivers, physicians, researchers, top ASCO social media influencers, AACR members, and advocacy organizations. Moderators and participants are drawn from 23 academic institutions. The PancChat team corresponds with participants and replies to tweets that are not addressed during the chat. Results: Since its inception, PancChat has had a total of 28 million impressions (the total number of times each tweet is seen) from 16 chats, averaging 1.75 million per chat. Popular topics include clinical trials (1.4 million), familial/hereditary PC (2.9 million), and early detection (2.2 million). The average engagement rate is 72% which measures how much people interact with a tweet by clicking or sharing links. From April 2016-August 2017 there were 8,502 tweets using #PancChat. Conclusions: Impression and engagement numbers show that this novel PancChat platform fulfills a need for the PC community. The narrow focus of each chat provides an opportunity to learn about the disease, research, and clinical trials. Participants return knowing that they will interact with PC experts. The popularity of PancChat among patients and doctors confirms the power of social media to reach a specific community.

This paper presents a model-based computationally efficient method for detecting milling chatter in its incipient stages and for chatter frequency estimation by monitoring the cutting force signals. Based on a complex exponentials model for the dynamic chip thickness, the chip regeneration effect is amplified and isolated from the cutting force signal for early chatter detection. The proposed method is independent of the cutting conditions. With the aid of a one tap adaptive filter, the method is shown to be capable of distinguishing between chatter and the dynamic transients in the cutting forces arising from sudden changes in workpiece geometry and tool entry/exit. To facilitate chatter suppression once the onset of chatter is detected, a time domain algorithm is proposed so that the dominant chatter frequency can be accurately determined without using computationally expensive frequency domain transforms such as the Fourier transform. The proposed method is experimentally validated.

Chatter is a common state in the end milling, which has major influence on machining quality. Early chatter detection is a prerequisite for taking adequate measures to avoid chatter. Nevertheless, there are still numerous challenges and difficulties in the feature extraction of chatter detection. In this paper, effective chatter detection in the milling process of a vibration signal is investigated using time frequency analysis. Firstly, the measured vibration signal in the machining process was preprocessed by Wavelet Transform decomposition. Different sub-bands were obtained and the portion with high chatter information was reconstructed for further analysis. Since measured signals from sensors usually constitute of background noise and other disturbances, the wavelet decomposition serves as a preprocessor to denoise the measured signals and enhance the performance of the Wavelet Synchro Squeezing Transform (WSST) which was applied on the reconstructed signal for chatter Identification. The techniques were used to detect the chatter in different operating condition of the machining process. Finally, some milling tests were conducted and the experiment results prove that the proposed method indeed succeed in effectively chatter identification.

Chatter is an unfavorable phenomenon in turning operation causing poor surface quality. Active chatter elimination methods require the chatter to be detected before the control reacts. In this paper, a chatter detection method based on a coherence function of the acceleration of the tool in the x direction and an audio signal is proposed. The method was experimentally tested on longitudinal turning of a stock bar and facing of a hollow bar. The results show that the proposed method detects the chatter in an early stage and allows correcting control actions before the chatter influences the surface quality of the workpiece. The method is applicable both to facing and longitudinal turning.

Le diagnostic cumulatif des systèmes dynamiques nécessite la détection, l’identification et la caractérisation des dégradations naissantes. Son application à l'usinage à grande vitesse, par exemple, pourrait s’appuyer sur l’analyse des phénomènes de bifurcations de période-N pour détecter et identifier les chatters (broutages) naissants et améliorer la qualité des produits et des processus de fraisage. Jusqu'à présent, de nombrecuses méthodes efficaces ont été proposées pour détecter les broutages naissants et identifier les bifurcations de période-N. Cependant, ces méthodes peinent à mettre en œuvre ces tâches de manière fiable et précise. Le but de la présente thèse est de développer et mettre en œuvre des méthodes de détection de broutages naissants et d’identification de bifurcations de période-N dans une approche de diagnostic cumulatif temps réel. Afin de détecter les défauts de broutages naissants (early-chatter), nous avons proposé trois méthodes de détection et une méthode d’identification pour le diagnostic cumulatif. La première méthode peut être utilisée pour détecter à distance les broutages naissants. La deuxième méthode détecte rapidement les broutages naissants dans des conditions spécifiques de fonctionnement et de mesure. Mais dans la pratique, les conditions de fonctionnement et de mesure sont complexes et variables. Pour s'adapter aux différentes conditions de fonctionnement et de mesure, nous avons proposé une troisième méthode et cette dernière détecte de manière fiable les broutages naissants. On note également que dans les processus de fraisage, les broutages peuvent naître avec une bifurcation de type période-N ou de type Hopf. La qualité d'usinage sous un processus de bifurcation de type période-N est moins critique que celle de type Hopf. Ainsi, il est indispensable d’identifier précocement les bifurcations de type période-N pour améliorer l'efficacité d'usinage. Pour cela, nous avons développé une méthode d’identification du type et de la taille des bifurcations de période-N. Nous avons également prouvé l'efficacité des méthodes proposées, en utilisant deux modèles de processus de fraisage de référence. De plus, les méthodes proposées peuvent être utilisées pour le diagnostic de défaut d'autres systèmes dynamiques, tels que les systèmes de conversion d'énergie par modulation de largeur d'impulsion ou systèmes de paliers ou d’engrenage
Cumulative diagnosis of dynamic systems requires the detection, identification, and characterization of incipient degradations. Its application to high-speed machining, for instance, could rely on period-N bifurcations phenomena analysis to detect and identify early-chatters and improve the quality of milling products and processes. Up to now, many efficient methods were proposed to detect early-chatter and identify period-N bifurcations. But these methods are struggling to implement these tasks reliably and accurately due to the complex nonlinear characteristics of their dynamic behaviors, the noise, and the variation of their operating conditions. The present thesis aims to develop and implement methods of early-chatter detection and period-N bifurcations identification within a real-time cumulative diagnosis approach. Aimed at early-chatter detection, we proposed three detection methods and one identification method for the cumulative diagnosis. The first method can be used to detect early-chatters remotely. The second one detects early-chatter quickly under specific operating and measuring conditions. However, in practice, the operating and measuring conditions are complex and variable. To adapt to different operating and measuring conditions, we proposed a third method, and the latter detects early-chatter reliably. It is also noted that in milling processes, the early-chatter can give rise to a bifurcation of period-N or Hopf type. The machining quality under the bifurcation process of the period-N type is less critical than that under the Hopf bifurcation type. To improve machining productivity and ensure the required machining quality, we can mill the workpiece under the condition of period-N bifurcations. Thus, it is compulsory to identify the early period-N bifurcations for improving machining productivity. For that purpose, we developed a method for identifying the type and size of the period-N bifurcations. We also proved the effectiveness of the proposed methods, using two benchmark milling process models. Besides, the proposed methods can be used for fault diagnosis of other dynamic systems, such as the pulse energy conversion systems or bearing or gearing systems

This paper presents a model-based computationally efficient method for detecting milling chatter in its incipient stages. Based on a complex exponentials model for the dynamic chip thickness, the chip regeneration effect is amplified and isolated from the cutting force signal for early chatter detection. The proposed method is independent of the cutting conditions. With the aid of a one tap adaptive filter, the proposed method is also found to be able to distinguish between chatter and the dynamic transients in the cutting forces due to sudden changes in workpiece geometry and tool entry/exit. The proposed method is experimentally validated.

Abstract On-line detection and active control of chatter vibration have always been important issues in milling process respectively. To some extent, the signals obtained with sensors determine the performance of on-line detection and active control of chatter. However, due to the characteristics of milling process, the obtained signals are mainly consisted with spindle rotation frequency and its harmonics, and the chatter components are usually submerged by these stable harmonics, imposing negative effects for the detection and active control of milling chatter. Then, it is highly needed to design a real-time filter to filter out the spindle rotation frequency and its harmonics. In this paper, an adaptive filter is designed to filter out the spindle speed related components. Moving average (MR) model and adaptive filter theory is utilized to estimate these periodic components. The influence of filter order and step size factor on the filter characteristics are also analyzed. Considering that the filter order needs to be adjusted under different cutting conditions, which will alter the filter’s performance, an improved adaptive filter is proposed. Experiments are also performed and the experimental results show that, not only the spindle speed related components can be filtered out effectively, but the chatter frequency components are amplified with appropriate initial step factor, which is beneficial for the detection of milling chatter at early stage. Meanwhile, the periodic components caused by the installation error and the other spindle speed related components can be effectively filtered out real-timely, preventing the saturation of actuator caused by these stable components.