A non-destructive surface burn detection method for ferrous metals based on acoustic emission and ensemble empirical mode decomposition: from laser simulation to grinding process

Yang, Zhensheng; Hai-xi, WU; Yu, Zhonghua; Huang, Youfang

doi:10.1088/0957-0233/25/3/035602

Cited by 19 publications

(22 citation statements)

References 29 publications

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“…AE has been widely applied as a non-destructive and nonintrusive monitoring technique for manufacturing processes [12], including tool wear identification [10], machining workpiece defect monitoring [60], and machine component defect detection [25], as well as coating [36], granulation [5], and crystallization [15] in chemical processing.…”

Section: Acoustic Emissionmentioning

confidence: 99%

In situ monitoring of FDM machine condition via acoustic emission

Hai-xi

Wang

2015

Int J Adv Manuf Technol

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Fused deposition modeling (FDM) is one of the most popular additive manufacturing technologies for fabricating prototypes with complex geometry and different materials. However, current commercial FDM machines have the limitations in process reliability and product quality. In order to overcome these limitations and increase the levels of machine intelligence and automation, machine conditions need to be monitored more closely as in closedloop control systems. In this study, a new method for in situ monitoring of FDM machine conditions is proposed, where acoustic emission (AE) technique is applied. The proposed method allows for the identification of both normal and abnormal states of the machine conditions. The time-domain features of AE hits are used as the indicators. Support vector machines with the radial basis function kernel are applied for state identification. Experimental results show that this new method can potentially serve as a nonintrusive diagnostic and prognostic tool for FDM machine maintenance and process control.

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Section: Acoustic Emissionmentioning

confidence: 99%

In situ monitoring of FDM machine condition via acoustic emission

Hai-xi

Wang

2015

Int J Adv Manuf Technol

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“…2 This heat is mostly transferred to the workpiece (65%-80%) due to the low thermal conductivity of the conventional abrasive grinding wheels. 3 In the turning process, Yang et al 4 reported that about only 5% of the generated heat is transferred to the workpiece, while the majority is directed to the chip.…”

Section: Introductionmentioning

confidence: 99%

A comparative study of two indirect methods to monitor surface integrity of ground components

Hübner

Silva

Duarte

et al. 2020

Structural Health Monitoring

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One of the major challenges in grinding is to conciliate the material removal rate with components free from damages. Due to the poor thermal conductivity of conventional grinding wheels, most of the heat generated during grinding is transferred to the workpiece surface and subsurface, which can cause thermal damages and impair the performance in service of machined components. Hence, it is very important to monitor the grinding process to ensure the quality of the machined parts. Thus, this work presents an innovative study comparing two indirect monitoring methods in monitoring surface integrity of steel during grinding: the acoustic emission technique and the electromechanical impedance technique. Worktable speed and radial depth of cut were used as input parameters. Visual inspection and scanning electron microscopy images of ground surfaces as well as microhardness were the output variables used to assess surface integrity and to establish a relationship with the acoustic emission and electromechanical impedance techniques. Since the acoustic emission signals are non-stationary in nature, these signals were analyzed in the time–frequency domain by applying the short-time Fourier transform and the continuous wavelet transform. The root mean square deviation index was extracted as feature from the acoustic emission and electromechanical impedance signals. Results showed that both techniques presented similar results. The root mean square deviation index showed a good correlation with alterations in surface integrity under the conditions investigated.

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“…Researchers simulated the grinding burn process by laser, from which pure AE burn signals were obtained. The results were used as reference to identify grinding burn [20][21][22]. Wu et al [23] carried out a series of grinding experiments to monitor grinding burn by AE sensor, dynamometer and surface roughness [23].…”

Section: Introductionmentioning

confidence: 99%

“…have been calculated and compared [7]. Then time-frequency methods such as Short Time Fourier Transform (STFT) [22], Wavelet Packet Transform (WPT) [20], Hilbert Huang Transform (HHT) [24] and Ensemble Empirical Mode Decomposition (EEMD) [21] have been introduced to analyze grinding burn signals.…”

Section: Introductionmentioning

confidence: 99%

Grinding Burn Detection Based on Cross Wavelet and Wavelet Coherence Analysis by Acoustic Emission Signal

Gao

Lin

Wang

et al. 2019

Chin. J. Mech. Eng.

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Grinding burn monitoring is of great importance to guarantee the surface integrity of the workpiece. Existing methods monitor overall signal variation. However, the signals produced by metal burn are always weak. Therefore, the detection rate of grinding burn still needs to be improved. The paper presents a novel grinding burn detection method basing on acoustic emission (AE) signals. It is achieved by establishing the coherence relationship of pure metal burn and grinding burn signals. Firstly, laser and grinding experiments were carried out to produce pure metal burn signals and grinding burn signals. No-burn and burn surfaces were generated and AE signals were captured separately. Then, the cross wavelet transform (XWT) and wavelet coherence (WTC) were applied to reveal the coherence relationship of the pure metal burn signal and grinding burn signal. The methods can reduce unwanted AE sources and background noise. Novel parameters based on XWT and WTC are proposed to quantify the degree of coherence and monitor the grinding burn. The grinding burn signals were recognized successfully by the proposed indexes under same grinding condition. which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

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A non-destructive surface burn detection method for ferrous metals based on acoustic emission and ensemble empirical mode decomposition: from laser simulation to grinding process

Cited by 19 publications

References 29 publications

In situ monitoring of FDM machine condition via acoustic emission

In situ monitoring of FDM machine condition via acoustic emission

A comparative study of two indirect methods to monitor surface integrity of ground components

Grinding Burn Detection Based on Cross Wavelet and Wavelet Coherence Analysis by Acoustic Emission Signal

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