Improved multi-wavelet denoising with neighboring coefficients of cutting force for application in the load spectrum of computer numerical control lathe

He, Jialong; Yang, Zhaojun; Chen, Chuanhai; Li, Guofa; Li, Zhen; Jia, Yunchao

doi:10.1177/1687814018754674

Cited by 11 publications

(4 citation statements)

References 31 publications

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“…Kim [ 75 ] used the time–frequency image obtained via short-time Fourier transform as the input signal of an ANN classifier and then applied the neural network classifier to identify different impact noises, distinguishing the actual fault impact noise from the background noise with an accuracy of 100%. He [ 76 ] proposed an improved multiwavelet signal processing method with adjacent coefficients and proved that this method can effectively suppress the interference of high-frequency noise on the target signal.…”

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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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 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 relationship (17) links the filters C(z) and D(z) to the polyphase filter H p (z). Differentiation after multiplication by any constant vector v gives:…”

Section: Vanishing Moment Conditions For Multiwaveletsmentioning

confidence: 99%

“…Multiwavelets are more flexible, and can combine properties such as compact support, orthogonality and symmetry. This added flexibility is for example advantageous in multiwavelet denoising, which has been applied in rolling bearing fault detection [13][14][15][16], and in the load spectrum of computer numerical control lathe [17]. Recently, the correspondence between multiwavelet shrinkage and nonlinear diffusion was studied [18].…”

Section: Introductionmentioning

confidence: 99%

The Design of Matched Balanced Orthogonal Multiwavelets

Karel

Steenkiste

Peeters

2022

Front. Appl. Math. Stat.

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The theory of orthogonal multiwavelets offers enhanced flexibility for signal processing applications and analysis by employing multiple waveforms simultaneously, rather than a single one. When implementing them with polyphase filter banks, it has been recognized that balanced vanishing moments are needed to prevent undesirable artifacts to occur, which otherwise compromise the interpretation and usefulness of the multiwavelet analysis. In the literature, several such balanced orthogonal multiwavelets have been constructed and published; but however useful, their choice is still limited. In this work we present a full parameterization of the space of all orthogonal multiwavelets with two balanced vanishing moments (of orders 0 and 1), for arbitrary given multiplicity and degree of the polyphase filter. This allows one to search for matching multiwavelets for a given application, by optimizing a suitable design criterion. We present such a criterion, which is sparsity-based and useful for detection purposes, which we illustrate with an example from electrocardiographic signal analysis. We also present explicit conditions to build in a third balanced vanishing moment (of order 2), which can be used as a constraint together with the earlier parameterization. This is demonstrated by constructing a balanced orthogonal multiwavelet of multiplicity three, having three balanced vanishing moments, but this approach can easily be employed for arbitrary multiplicity.

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“…Tool wears can also make an influence on the cutting force. It is shown that instantaneous milling force can be predicted with the model that comprised the relationship between milling force against tool flank wear ( Zhu and Zhang, 2019). The energy consumption assessment for machining system through models has potential applications in complex production environments those are usually broken up into constant no-load loss, cutting energy loss and auxiliary energy loss.…”

Section: Introductionmentioning

confidence: 99%

A method of milling force predictions for machining tools based on an improved ARMA model

Gao

Zhou

et al. 2023

AEAT

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Purpose Cutting force prediction is pretty important for manufacture management. Thus, the purpose of this paper is to obtain the cutting force of the machining process with high efficiency and low cost. A method based on the improved auto regressive moving average (ARMA) model is proposed for cutting force predictions in milling process. Design/methodology/approach First, classification and normalization are made for initial cutting force. Second, the cutting force sequences are compressed followed singular and valid value removed. At last, the improved ARMA model is used for cutting force fit and extrapolation considered the time domain characteristics. Findings A series of cutting force with the spindle speed 595r/min is carried out in the research. It is showed that the mean absolute percentage error value of cutting force extrapolation results which is based on the improved model is smaller. The percentage value is approximately 5.80%. Then the root mean square error test value is only 72.49, which is smaller than that with other traditional method, such as hidden Markov model. The extrapolation results with the proposed model performed good consistency and accuracy in terms of peaks, valleys and volatility compared with the experiment results. Originality/value The proposed method that is based on the improved ARMA model can be used for cutting force predictions conveniently. And the predictions can be used for improving the qualities in milling process.

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Improved multi-wavelet denoising with neighboring coefficients of cutting force for application in the load spectrum of computer numerical control lathe

Cited by 11 publications

References 31 publications

Research Progress of Noise in High-Speed Cutting Machining

Research Progress of Noise in High-Speed Cutting Machining

The Design of Matched Balanced Orthogonal Multiwavelets

A method of milling force predictions for machining tools based on an improved ARMA model

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