Noise analysis and sources identification in machine tool spindles

Cao, Hongrui; Kang, Ting

doi:10.1016/j.cirpj.2019.04.001

Cited by 10 publications

(11 citation statements)

References 11 publications

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“…Bearings, gears, and motors are all sources of noise in the machine tool [ 36 , 37 ]. Cao et al [ 38 ] performed an idle test on an ordinary spindle and an abnormal spindle with abnormal bearing. They also conducted a systematic study on noise caused by rolling bearings, rotors, and motors, and analyzed the noise source of the spindle at different speeds.…”

Section: Noise Source Analysismentioning

confidence: 99%

“…Therefore, Microphones are widely used. Cao et al [ 38 ] collected the noise signal of the spindle with three microphones in the experiment. The three microphones used in the experiment were MPA201, with a frequency response between 20 Hz to 20 kHz and a sensitivity of 45.2 mV/Pa and 49.5 mV/Pa.…”

Section: Noise Signal Acquisition and Numerical Analysis And Noise Co...mentioning

confidence: 99%

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Research Progress of Noise in High-Speed Cutting Machining

Wei

Shang

Peng

et al. 2022

Sensors

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High-speed cutting technology has become a development trend in the material processing industry. However, high-intensity noise generated during high-speed cutting exerts a potential effect on the processing efficiency, processing accuracy, and product quality of the workpiece; it may even cause hidden safety hazards. To conduct an in-depth study of noise in high-speed cutting machining, this work reviews noise sources, noise collection and numerical recognition, noise control, and condition monitoring based on acoustic signals. First, this article introduces noise sources, noise signal acquisition equipment, and analysis software. It is pointed out that how to accurately classify and recognize the target signal in the complex high-speed machining environment is one of the focuses of scholars’ research. Then, it points out that a computer achieves high accuracy and practicability in signal analysis, processing, and result display. Second, in the aspect of noise signal processing, the characteristics of noise signals are analyzed. It is pointed out that accurately analyzing the characteristics of different noise source signals and adopting appropriate methods for identification and processing are the necessary conditions for effectively controlling and reducing the noise in the process of high-speed cutting. The advantages and applicable fields of artificial intelligence algorithms in processing mixed noise source signals with different frequency characteristics are compared, providing ideas for studying the mechanism of noise generation and the identification of noise sources. Third, in terms of noise control, a detailed overview is provided from the aspects of the treatment of the noise source that contributes the most to the overall noise, the improvement of the tool structure, the optimization of cutting parameters, and the analysis of contact factors between the tool and the workpiece. It provides an effective way for noise control in the process of high-speed cutting. In addition, the application of acoustic signals to condition monitoring is also thoroughly analyzed. The practical application value of condition monitoring based on acoustic signals in high-speed machining is highlighted. Finally, this paper summarizes the positive significance of noise research in high-speed machining and identifies key problems and possible research methods that require further study in the future.

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Section: Noise Source Analysismentioning

confidence: 99%

Section: Noise Signal Acquisition and Numerical Analysis And Noise Co...mentioning

confidence: 99%

Research Progress of Noise in High-Speed Cutting Machining

Wei

Shang

Peng

et al. 2022

Sensors

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“…The vibrations generated in this way are characterised by different frequencies, paths of their transmission being different as well. The mobile machine moving over uneven ground creates excitations in the frequency range from 0.5 to 250 Hz (Yupapin & Pornsuwancharoen, 2019;Abdelkareem et al, 2021), which also contributes to the generation and propagation of noise due to the mechanical connection of the elements of the hydraulic system, which is done through hoses and common fastenings (Wegener et al, 2021;Cao et al, 2019). In this frequency band, there are excitations from the driving engine (internal combustion engine) related to the kinematics of the displacement pump, which manifest themselves in the pressure pulsations in the machine's hydraulic system (Bach et al, 2017;Xu et al, 2023;Pang et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

The Impact of Mechanical Vibrations on Hydraulic Valves and the Possibility of Reducing the Effects

Stosiak,

Skačkauskas,

Deptuła

2024

Aviation

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The paper shows that mechanical vibrations occur in a wide frequency range in the hydraulic systems operating in the real world. Hydraulic valves are also exposed to these vibrations. The paper gives examples of vibration sources and suggests that the influence of vibrations on hydraulic valves could be reduced. Particular attention was paid to the vibrating proportional distributor. The amplitude-frequency spectrum of pressure pulsation in a hydraulic system with a vibrating proportional distributor was analysed. During the tests, the frequency of external mechanical vibrations acting on the proportional distributor and their direction was changed.

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“…Furthermore, rotation speed does not only cause machinery failure, but it also generates noise. Noise pollution significantly alters human mood (e.g., stress, anxiety and loss of attention) and body functioning (e.g., heart and breathing rates, and blinks), which in turn affects work performance and life quality of nearby inhabitants [14,15]. As machinery failure and noise related to rotation speed are generated by vibration, the accurate and prompt detection of vibration signals can lead to a reduction of both of them, including their side effects.…”

Section: Introductionmentioning

confidence: 99%

Deep-Learning Method Based on 1D Convolutional Neural Network for Intelligent Fault Diagnosis of Rotating Machines

Alonso-Valerdi

Ibarra-Zárate

2022

Applied Sciences

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Fault diagnosis in high-speed machining centers (HSM) is critical in manufacturing systems, since early detection saves a substantial amount of time and money. It is known that 42% of failures in these centers occur in rotatory machineries, such as spindles, in which, the bearings are fundamental elements for effective operation. Nowadays, there are several machine- and deep-learning methods to diagnose the faults. To improve the performance of those traditional machine-learning tools, a deep-learning network that works on raw signals, which do not require previous analysis, has been proposed. The 1D Convolutional Neural Network (CNN) proposed model showed great capacity of adapting to three types of configurations and three different databases, despite a training set with a smaller number of categories. The network still detected faults at early damage stages. Additionally, the low computational cost shows the Deep-Learning Neural Network’s (DLNN) suitability for real-time applications in industry. The proposed structure reached a precision of 99%; real-time processing was around 8 ms per signal, and standard deviation of repeatability was 0.25%.

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Noise analysis and sources identification in machine tool spindles

Cited by 10 publications

References 11 publications

Research Progress of Noise in High-Speed Cutting Machining

Research Progress of Noise in High-Speed Cutting Machining

The Impact of Mechanical Vibrations on Hydraulic Valves and the Possibility of Reducing the Effects

Deep-Learning Method Based on 1D Convolutional Neural Network for Intelligent Fault Diagnosis of Rotating Machines

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