Vibration signature based stability studies in internal turning with a wavelet denoising preprocessor

Yuvaraju, Bonda Atchuta Ganesh; Nanda, Bijoy Kumar; Jonnalagadda, Srinivas

doi:10.1016/j.measurement.2020.107520

Cited by 19 publications

(7 citation statements)

References 32 publications

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“…To improve the surface quality and reduce vibration and wear of the cutting tool, the document [57] proposes an approach based on the multilayer perceptron (MLP) and Radial Basis Function (RBF) to detect the chatter in a milling process. In the document [72], the authors achieve 100% accuracy for chatter detection by combining WT and HHT for signal feature extraction and a probabilistic neural network for classification, but once again, in a very simplistic machining situation, far from industrial applications, cutting conditions being far outside the tool manufacturer preconization, and with a clear correlation between chatter and vibration amplitude.…”

Section: Machine Learningmentioning

confidence: 99%

Review of AI-based methods for chatter detection in machining based on bibliometric analysis

Kounta

Arnaud

Kamsu-Foguem

et al. 2022

Int J Adv Manuf Technol

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HIGHLIGHTS• Bibliometric analysis on chatter detection techniques in machining processes.• Effectiveness of AI methods combined with transformation and decomposition techniques.• Research areas mainly cover manufacturing, mechanics, and automation control systems.• Application of signal processing techniques in chatter detection with their advantages.• Challenges of deep learning models to solve problems of performance and explainability.

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Section: Machine Learningmentioning

confidence: 99%

Review of AI-based methods for chatter detection in machining based on bibliometric analysis

Kounta

Arnaud

Kamsu-Foguem

et al. 2022

Int J Adv Manuf Technol

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show abstract

“…In contrast, the dynamic displacement in radial direction varies with depth of cut and produce the regenerative effect. Therefore, a single degree-offreedom model shown in Figure 1 is considered because of the vibrations in radial cutting direction of the boring bar are most significant in internal turning [35]. Further, the dynamic modelling of the conventional as well as CLD (that is passively damped constrained layered) boring bar are elaborated below using Euler-Bernoulli beam theory and finite element modelling.…”

Section: Modelling Of the Boring Barmentioning

confidence: 99%

Investigation of stability in internal turning using a boring bar with a passive constrained layer damping

Yuvaraju¹,

Nanda²,

Srinivas³

2021

FME Transactions

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The vibrations produced in a boring tool in internal turning deteriorate the machined surface quality and reduce the tool life, which results in a massive noise during the machining. Therefore, unwanted vibrations are necessary to be eliminated by improving the boring bar's dynamic stiffness and damping capacity. This paper investigates a passive constrained layer damping (CLD) boring bar with a hybrid damping layer to study the internal turning system's stability. Initially, the dynamic models of the conventional and CLD tools are thoroughly studied using Euler-Bernoulli beam theory (EBT) and validated them with finite element modelling (FEM). The frequency response functions (FRFs) obtained from the impact hammer tests are used to estimate the modal parameters. With modal parameters, the semi-analytical stability lobe diagrams (SLDs) are plotted for the boring system with the conventional and CLD boring bar. Tool-tip responses for various cutting conditions are simulated numerically to validate and to study stability. The cutting experiments with traditional and CLD boring bar are conducted for stability analysis and compared tool-tip responses with numerical results. It is observed that both the numerical and experimental results agree with the selected cutting conditions from SLDs. It is also observed that the CLD boring bar with a hybrid damping layer reduced the vibration displacements by five times compared to the conventional one.

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“…Therefore, the wavelet threshold de-noising method is adopted in this manuscript, and the vibration signal is denoised by using the db5 wavelet, which can satisfy the key properties better than others [39]. Several trials have been conducted to identify the wavelet transforms that highly suitable to the present problem, and db 5 is identified as the best choice [40]. The three main steps of wavelet de-noising include wavelet decomposition, threshold quantization and reconstruction of wavelet signal [41].…”

Section: A Data Preprocessingmentioning

confidence: 99%

Fault Diagnosis of Rolling Bearing Based on GA-VMD and Improved WOA-LSSVM

Chen

Han

et al. 2020

IEEE Access

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To improve the fault identification accuracy of rolling bearings due to the problems of parameter optimization and low convergence accuracy, a novel fault diagnosis method for rolling bearings combining wavelet threshold de-noising, genetic algorithm optimization variational mode decomposition (GA-VMD) and the whale optimization algorithm based on the von Neumann topology optimization least squares support vector machine (VNWOA-LSSVM) is proposed in this manuscript. First, wavelet threshold de-noising is used to preprocess the vibration signal to reduce the noise and improve the signal-to-noise ratio (SNR). Second, a genetic algorithm (GA) is utilized to optimize the parameters of variational mode decomposition (VMD), and optimized VMD is adopted to extract the fault feature information. The VNWOA-LSSVM fault diagnosis model is built to train and identify the fault feature vectors. The proposed method is validated by experimental data. The results show that this method can not only effectively diagnose various damage positions and extents of rolling bearings but also has good identification accuracy.INDEX TERMS Wavelet threshold de-noising, genetic algorithm, variational modal decomposition, von Neumann topology, rolling bearing.

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Vibration signature based stability studies in internal turning with a wavelet denoising preprocessor

Cited by 19 publications

References 32 publications

Review of AI-based methods for chatter detection in machining based on bibliometric analysis

Review of AI-based methods for chatter detection in machining based on bibliometric analysis

Investigation of stability in internal turning using a boring bar with a passive constrained layer damping

Fault Diagnosis of Rolling Bearing Based on GA-VMD and Improved WOA-LSSVM

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