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Machines
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Noise and Vibration in Machine Tools
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Noise and Vibration in Machine Tools

A special issue of Machines (ISSN 2075-1702). This special issue belongs to the section "Machines Testing and Maintenance".

Deadline for manuscript submissions: closed (30 June 2023) | Viewed by 5480

Special Issue Editor

Dr. Hélène Chanal

E-Mail Website
Guest Editor
Universit´e Clermont Auvergne, CNRS, Clermont Auvergne INP, Institut Pascal, F-63000 Clermont-Ferrand, France
Interests: system dynamics modeling; robotics; machining; mechanical engineering; manufacturing process mechanics; calibration industrial engineering; CNC machining; CAD; mechanical processes

Special Issue Information

Dear Colleagues,

Vibration is a major issue for high-quality machining parts. Noise emission is also frequently considered a defect, with research urgently needed on how to control noise emission levels to guarantee human safety. These two phenomena can be controlled through vibration prediction of machine-tool cells, which is the focus of this Special Issue.

Papers are welcome on topics related to aspects of theory, design, practice, and application of noise and vibration in machine tools.

Dr. Hélène Chanal
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Machines is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • noise
  • control
  • design
  • vibration
  • machine tools

Published Papers (3 papers)

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Research

19 pages, 6292 KiB  
Article
Mathematical Modeling and Machining of the Internal Double-Arc Spiral Bevel Gear by Finger Milling Cutters for the Nutation Drive Mechanism
by Dawei Zhang, Zhenya Wang, Ligang Yao and Daizhi Xie
Machines 2022, 10(8), 663; https://doi.org/10.3390/machines10080663 - 05 Aug 2022
Cited by 1 | Viewed by 2046
Abstract
A method of machining the internal double-arc spiral bevel gear with a finger milling cutter was presented. The mathematical model of the internal spiral bevel gear tooth profile was established considering the principle of machining a spiral bevel gear by the generating method, [...] Read more.
A method of machining the internal double-arc spiral bevel gear with a finger milling cutter was presented. The mathematical model of the internal spiral bevel gear tooth profile was established considering the principle of machining a spiral bevel gear by the generating method, and a three-dimensional (3D) tooth profile graph was developed. Subsequently, by applying the gear meshing theory, the 3D model of the tooth alignment curve for the finger milling cutter was established. Based on the tooth surface equation of crown gear, the cutter intercept equation was derived. The cutter was divided into four finger milling cutters considering the design difficulty of the cutter, which is used to manufacture different arc segments of the double-arc tooth profile, respectively. The special machining tool model of the internal spiral bevel gear was further developed by using SolidCam, and the simulation experiment was carried out. The simulated gear model was compared with the theoretical gear model and the error of the simulation experiment was estimated. Actual machining on the machine tool and the internal spiral bevel gear were inspected. The maximum error is 0.035 mm, and the minimum error is 0.005 mm. The machining accuracy meets the requirements. The feasibility of machining the internal double-arc spiral bevel gear with a finger milling cutter was verified. Full article
(This article belongs to the Special Issue Noise and Vibration in Machine Tools)
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18 pages, 4230 KiB  
Article
An Optimized VMD Method for Predicting Milling Cutter Wear Using Vibration Signal
by Hao Chang, Feng Gao, Yan Li, Xiaoqing Wei, Chuang Gao and Lihong Chang
Machines 2022, 10(7), 548; https://doi.org/10.3390/machines10070548 - 06 Jul 2022
Cited by 5 | Viewed by 1530
Abstract
Tool wear has a negative impact on machining quality and efficiency. As for the nonlinear and non-stationary characteristics of vibration signals and strong background noises during the milling process, an identification method of the milling cutter wear state based on the optimized Variational [...] Read more.
Tool wear has a negative impact on machining quality and efficiency. As for the nonlinear and non-stationary characteristics of vibration signals and strong background noises during the milling process, an identification method of the milling cutter wear state based on the optimized Variational Mode Decomposition (VMD) was proposed, in which the objective function is to minimize the Envelope Entropy (Ep); the various modes of the vibration signal are decomposed using the self-adaptive optimization parameters with Differential Evolution (DE). According to the cross-correlation coefficient in the frequency domain between Intrinsic Mode Function (IMF) and the original signals, the informative IMF components were selected as the sensitive IMF components to superimpose the reconstruction signal and extract the eigenvalues. The mapping relationship between the eigenvalues and the milling cutter wear degree is established by the Naive Bayes classifier method. The experimental results under the various operation conditions indicate that the proposed optimized VMD method possesses an excellent generalization performance. Compared with Empirical Mode Decomposition (EMD) and Ensemble Empirical Mode Decomposition (EEMD), it has better denoising capacity, and so can improve the identification accuracy of the milling cutter wear. Therefore, the processing quality and production efficiency are ensured effectively. Full article
(This article belongs to the Special Issue Noise and Vibration in Machine Tools)
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16 pages, 4800 KiB  
Article
Coal–Rock Cutting Sound Denoising Based on Complete Ensemble Empirical Mode Decomposition with Adaptive Noise and an improved Fruit Fly Optimization Algorithm
by Chaofan Ren, Jing Xu, Jie Xu, Yanxin Liu and Ning Sun
Machines 2022, 10(6), 412; https://doi.org/10.3390/machines10060412 - 25 May 2022
Cited by 2 | Viewed by 1318
Abstract
The cutting sound signal of a coal mining shearer is an important signal source for identifying the coal–rock cutting mode and load state. However, the coal–rock cutting sound signal directly collected from the industrial field always contains a large amount of background noise, [...] Read more.
The cutting sound signal of a coal mining shearer is an important signal source for identifying the coal–rock cutting mode and load state. However, the coal–rock cutting sound signal directly collected from the industrial field always contains a large amount of background noise, which is not conducive to the subsequent feature extraction and recognition. Therefore, efficient noise elimination for the original signal is required. An intelligent processing method based on an improved complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) denoising algorithm is constructed for the cutting sound signal in this paper. CEEMDAN first decomposes the sound to generate a series of intrinsic modal functions (IMFs). Because the denoising threshold of each IMF is usually obtained by an experimental test or an empirical formula in the traditional CEEMDAN method, obtaining an optimal threshold set for each IMF is difficult. The processing effect is often restricted. To overcome this problem, the fruit fly optimization algorithm (FOA) was introduced for CEEMDAN threshold determination. Moreover, in the basic FOA, the scouting bee mutation operation and adaptive dynamic adjustment search strategy are applied to maintain the convergence speed and global search ability. The simulation result shows that the signal waveform processed by the improved CEEMDAN denoising algorithm is smoother than the other four typical eliminate noise signal algorithms. The output signal’s signal-to-noise ratio and mean square error are significantly improved. Finally, an industrial application of a shearer in a coal mining working face is performed to demonstrate the practical effect. Full article
(This article belongs to the Special Issue Noise and Vibration in Machine Tools)
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