The milling tool wear monitoring using the acoustic spectrum

Ai, Changsheng; Sun, Yuxin; He, Genghuang; Ze, Xiang Bo; Li, W.; Mao, Ken

doi:10.1007/s00170-011-3738-z

Cited by 36 publications

(20 citation statements)

References 13 publications

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“…For the implementation of monitoring schemes, most of the current studies on TCM are based on empirical analysis [10], or sensing-oriented approaches such as motor current analysis [11], vibration analysis [12]- [15], and acoustic emission (AE) [8], [16], [17], or some of the combination approaches [18], [19]. In [10], Rahman et al found that the premature failure was the major factor that affected the micromilling tool life.…”

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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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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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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“…Then the function (12) has a unique solution, and the solution of l 1 minimization is the same as that of l 0 minimization (11). As a result we can solve this problem with l 1 minimization to replace the original l 0 minimization with the same solution (Basis Pursuit),…”

Section: Basis Pursuit (Bp)mentioning

confidence: 96%

“…It has been reported by researchers that cutting forces contain reliable information on cutting conditions and the most effective for tool condition monitoring [7][8][9]. The advantage of AE is that the signal measured is a source of engagement where the chip is formed, as were introduced in [10,11]. Vibration has also been one of the most widely studied signals for monitoring due to its convenient implementation; see for examples in [12,13].…”

Section: The Backgroundmentioning

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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“…Non-contact methods such as acoustic-based, image-based and laser vibrometry-based systems, however, can be used conveniently and do not affect the machine, thereby increasing efficiency and allowing online supervisory control [2][3][4]. The acoustic signal has attracted much attention in industrial monitoring due to its strong robustness to illumination intensity, air quality and barrier objects compared with the image [5].…”

Section: Introductionmentioning

confidence: 99%

A Novel Denoising Method for an Acoustic-Based System through Empirical Mode Decomposition and an Improved Fruit Fly Optimization Algorithm

Wang

Tan

et al. 2017

Applied Sciences

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Generally, the sound signal produced by transmission unit or cutting unit contains abundant information about the working state of a machine. The acoustic-based diagnosis system presents some distinct advantages in some severe conditions particularly due to its unique non-contact measurement and unlimited use at the installation site. However, the original acoustic signal collected from manufacture process is always polluted by various background noises. In order to eliminate noise components from machinery sound effectively, an empirical mode decomposition (EMD) threshold denoising method optimized by an improved fruit fly optimization algorithm (IFOA) is launched in this paper. The acoustic signal was first decomposed by the adaptive EMD to obtain a series of intrinsic mode functions (IMFs). Then, the soft threshold function was applied to shrink the IMF coefficients. While the threshold of each IMF was determined by statistical estimation and empirical value for traditional EMD denoising, the denoising effect was often not desired and time-consuming. To solve these disadvantages, fruit fly optimization algorithm (FOA) was introduced to search global optimal threshold of each IMF. Moreover, to enhance the group diversity during production of the next generation of fruit flies and balance the local and global searching ability, a variation coefficient and a disturbance coefficient was introduced to the basic FOA. Then, a piece of simulated acoustic signal produced by the train was applied to validate the proposed EMD and IFOA threshold denoising (EMD-IFOA). The simulation results, which decreased 35.40% and 18.92% in mean squared error (MSE) and percent root mean square difference (PRD) respectively, and increased 40.36% in signal-to-noise ratio improvement (SNR imp ) compared with basic EMD denoising scheme at SNR = 5 dB, illustrated the effectiveness and superiority of the proposed approach. Finally, the proposed EMD-IFOA was conducted on an actual acoustic-based diagnosis system for cutting state recognition of the coal mining shearer to demonstrate the practical effect.

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The milling tool wear monitoring using the acoustic spectrum

Cited by 36 publications

References 13 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

A Novel Denoising Method for an Acoustic-Based System through Empirical Mode Decomposition and an Improved Fruit Fly Optimization Algorithm

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