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Автор: Grafiati
Опубліковано: 8 червня 2024

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1

Yang, Kai, Guofeng Wang, and Junyu Cong. "Milling chatter monitoring based on sparse representation and image similarity measurement." Insight - Non-Destructive Testing and Condition Monitoring 64, no. 3 (March 1, 2022): 146–54. http://dx.doi.org/10.1784/insi.2022.64.3.146.

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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.
2

Gupta, Pankaj, and Bhagat Singh. "Analyzing chatter vibration during turning on computer numerical control lathe using ensemble local mean decomposition and probabilistic approach." Noise & Vibration Worldwide 52, no. 6 (March 12, 2021): 168–80. http://dx.doi.org/10.1177/0957456521999871.

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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.
3

Liu, Yao, Xiufeng Wang, Jing Lin, and Wei Zhao. "Early chatter detection in gear grinding process using servo feed motor current." International Journal of Advanced Manufacturing Technology 83, no. 9-12 (August 18, 2015): 1801–10. http://dx.doi.org/10.1007/s00170-015-7687-9.

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4

Yang, Bin, Kai Guo, Qian Zhou, and Jie Sun. "Early chatter detection in robotic milling under variable robot postures and cutting parameters." Mechanical Systems and Signal Processing 186 (March 2023): 109860. http://dx.doi.org/10.1016/j.ymssp.2022.109860.

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5

Cao, Hongrui, Kai Zhou, Xuefeng Chen та Xingwu Zhang. "Early chatter detection in end milling based on multi-feature fusion and 3σ criterion". International Journal of Advanced Manufacturing Technology 92, № 9-12 (18 травня 2017): 4387–97. http://dx.doi.org/10.1007/s00170-017-0476-x.

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6

Yan, Shichao, and Yuwen Sun. "Early chatter detection in thin-walled workpiece milling process based on multi-synchrosqueezing transform and feature selection." Mechanical Systems and Signal Processing 169 (April 2022): 108622. http://dx.doi.org/10.1016/j.ymssp.2021.108622.

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7

Ocean, Allyson J., Niraj Jaysukh Gusani, Muhammad Shaalan Beg, Anirban Maitra, Julissa Viana, Allison Rosenzweig, Fatima Zelada-Arenas, et al. "Introduction of #PancChat: A novel Twitter platform to inform and engage the pancreatic cancer community." Journal of Clinical Oncology 36, no. 4_suppl (February 1, 2018): 242. http://dx.doi.org/10.1200/jco.2018.36.4_suppl.242.

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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.
8

Ma, Lei, Shreyes N. Melkote, and James B. Castle. "A Model-Based Computationally Efficient Method for On-Line Detection of Chatter in Milling." Journal of Manufacturing Science and Engineering 135, no. 3 (May 24, 2013). http://dx.doi.org/10.1115/1.4023716.

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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.
9

Matthew, Dialoke Ejiofor, Jianghai Shi, Maxiao Hou, and Hongrui Cao. "CHATTER DETECTION IN VIBRATION SIGNALS USING TIME-FREQUENCY ANALYSIS." MM Science Journal 2023, no. 4 (November 15, 2023). http://dx.doi.org/10.17973/mmsj.2023_11_2023099.

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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.
10

Hynynen, Katja M., Juho Ratava, Tuomo Lindh, Mikko Rikkonen, Ville Ryynänen, Mika Lohtander, and Juha Varis. "Chatter Detection in Turning Processes Using Coherence of Acceleration and Audio Signals." Journal of Manufacturing Science and Engineering 136, no. 4 (May 21, 2014). http://dx.doi.org/10.1115/1.4026948.

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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.
11

Navarro-Devia, John Henry, Yun Chen, Dzung Viet Dao, and Huaizhong Li. "Chatter detection in milling processes—a review on signal processing and condition classification." International Journal of Advanced Manufacturing Technology, February 7, 2023. http://dx.doi.org/10.1007/s00170-023-10969-2.

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Abstract Among the diverse challenges in machining processes, chatter has a significant detrimental effect on surface quality and tool life, and it is a major limitation factor in achieving 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.
12

Lu, Yezhong, Haifeng Ma, Yuxin Sun, Zhanqiang Liu, and Qinghua Song. "An Early Chatter Detection Method Based on Multivariate Variational Mode Decomposition and Chatter Correlation Factor." IEEE/ASME Transactions on Mechatronics, 2022, 1–12. http://dx.doi.org/10.1109/tmech.2022.3188680.

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13

Sarker, Abeed, Sahithi Lakamana, Ruqi Liao, Aamir Abbas, Yuan-Chi Yang, and Mohammed Al-Garadi. "Early detection of fraudulent COVID-19 products from Twitter chatter: a dataset and a baseline approach using anomaly detection (Preprint)." JMIR Infodemiology, October 20, 2022. http://dx.doi.org/10.2196/43694.

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14

Zhao, Yanqing, Kondo H. Adjallah, Alexandre Sava, and Zhouhang Wang. "MaxEnt feature-based reliability model method for real-time detection of early chatter in high-speed milling." ISA Transactions, July 2020. http://dx.doi.org/10.1016/j.isatra.2020.07.022.

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15

Trout, John N., and Jason R. Kolodziej. "Reciprocating compressor valve condition monitoring using image-based pattern recognition." Annual Conference of the PHM Society 8, no. 1 (October 3, 2016). http://dx.doi.org/10.36001/phmconf.2016.v8i1.2500.

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This work presents the development of a vibration-based condition monitoring method for early detection and classification of valve wear within industrial reciprocating compressors through the combined use of time-frequency analysis with image-based pattern recognition techniques. A common valve related fault condition is valve seat wear that is caused by repeated impact and accentuated by chatter. Seeded faults consistent with valve seat wear are introduced on the crankside discharge valves of a Dresser-Rand ESH-1 industrial compressor. A variety of operational data including vibration, cylinder pressure, and crank shaft position are collected and processed using a time-frequency domain approach. The resulting diagrams are processed as images with features extracted using 1st and 2nd order image statistics. A Bayesian classification strategy is employed with accuracy rates greater than 90% achieved using two and three-dimensional features spaces.

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