Envelope dynamic analysis: a new approach for milling process monitoring

Bîșu, Claudiu; Zapciu, Miron; Cahuc, Olivier; Gérard, Alain; Anica, Marin

doi:10.1007/s00170-011-3814-4

Cited by 30 publications

(16 citation statements)

References 33 publications

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“…As a result, the WTMM of white Gaussian noise decreases with the increase of scale s = 2 j . On the other hand, (13) indicates that the WTMM increases with the increase of scale (WTMM is proportional to the scale s). Therefore, when the force is analyzed at larger scales, the noise is reduced largely (with speed of s 2 ) in the final HE.…”

Section: ) On the Noise-robust Property Of He Valuesmentioning

confidence: 99%

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Online condition monitoring in micro-milling: A force waveform shape analysis approach

Zhu

Mei

Ye³

2015

IEEE Trans. Ind. Electron.

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The cutting force variations have long been studied for machining condition monitoring, due to their sensitivity to the tool conditions. These variations could be the statistics of the amplitude, features extracted from frequency components, wavelet coefficients, etc. Different from most current approaches, this paper introduces a novel online approach that directly correlates tool conditions with the force waveform variations and estimates the tool condition based on their corresponding singularity degree density functions. The singularities of waveforms are measured with Holder exponents (HEs), which are extracted from their wavelet transform modulus maxima. Experimental studies have shown that the HEs reflect the changes of tool conditions. The HE features are found robust to working condition variations, and their probability density functions are discriminate for tool state estimation in micromilling.

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Section: ) On the Noise-robust Property Of He Valuesmentioning

confidence: 99%

“…The vibration spectral features were selected based on the class mean scatter criteria to indicate tool wear condition. Bisu et al [13] adapted an envelope spectral analysis to monitor the milling process, and a vibration envelope analysis was developed to detect the cutting capacity of the tool.…”

mentioning

confidence: 99%

Online condition monitoring in micro-milling: A force waveform shape analysis approach

Zhu

Mei

Ye³

2015

IEEE Trans. Ind. Electron.

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“…One of the prominent and most widely studied aspects is the detection of tool conditions such as tool wear [8,10,48] and tool breakage [14,15], which are the main problems during machining under normal conditions. Studies on machining condition monitoring have also been carried from a system point of view, such as chatter [49], vibration [50], and control [3,17]. Traditional signal processing techniques such as Fourier and wavelet analysis are applied as the analysis tool.…”

Section: Applicationsmentioning

confidence: 99%

Compressive sensing and sparse decomposition in precision machining process monitoring: From theory to applications

Zhu

Lin

et al. 2015

Mechatronics

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“…Tansel et al [12] investigated the impedance, response to excitation and the propagation of Lamb waves on the tool surface to estimate the wear of drill bits. Bisu et al [13] executed a spectral analysis for machine tool diagnosis and tool condition monitoring and did further data processing with the Hilbert transform and spectral envelope analysis (Short-Time Fourier Transform (STFT)) to obtain a waterfall-type diagram to study the behaviour of the frequency spectrum over the elapse of time to enhance their spectral analysis. Kalvoda and Hwang [14] applied the Hilbert-Huang transform (HHT) to the measured signal of monitor tool wear in the frequency domain and compared the outcome of the results with the widely applied Fourier transform.…”

Section: Tool Condition Monitoringmentioning

confidence: 99%

Linear friction weld process monitoring of fixture cassette deformations using empirical mode decomposition

Bakker

Gibson

Wilson

et al. 2015

Mechanical Systems and Signal Processing

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Due to its inherent advantages, linear friction welding is a solid-state joining process of increasing importance to the aerospace, automotive, medical and power generation equipment industries. Tangential oscillations and forge stroke during the burn-off phase of the joining process introduce essential dynamic forces, which can also be detrimental to the welding process. Since burn-off is a critical phase in the manufacturing stage, process monitoring is fundamental for quality and stability control purposes. This study aims to improve workholding stability through the analysis of fixture cassette deformations. Methods and procedures for process monitoring are developed and implemented in a fail-or-pass assessment system for fixture cassette deformations during the burn-off phase. Additionally, the denoised signals are compared to results from previous production runs. The observed deformations as a consequence of the forces acting on the fixture cassette are measured directly during the welding process. Data on the linear friction-welding machine are acquired and de-noised using empirical mode decomposition, before the burn-off phase is extracted. This approach enables a direct, objective comparison of the signal features with trends from previous successful welds. The capacity of the whole process monitoring system is validated and demonstrated through the analysis of a large number of signals obtained from welding experiments.

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Envelope dynamic analysis: a new approach for milling process monitoring

Cited by 30 publications

References 33 publications

Online condition monitoring in micro-milling: A force waveform shape analysis approach

Online condition monitoring in micro-milling: A force waveform shape analysis approach

Compressive sensing and sparse decomposition in precision machining process monitoring: From theory to applications

Linear friction weld process monitoring of fixture cassette deformations using empirical mode decomposition

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