A two-step feature selection method for monitoring tool wear and its application to the coroning process

Yum, Juil; Kim, Tae Hyung; Kannatey‐Asibu, Elijah

doi:10.1007/s00170-012-4106-3

Cited by 8 publications

(6 citation statements)

References 24 publications

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“…So AE can be used to characterize these processes more accurately and reliably. Yum et al 11 used a two-step feature selection method to select AE signals for monitoring tool wear in grinding. In Hung and Lu, 12 a relationship between the AE signal generation and tool wear was developed for cutting processes in micromilling.…”

Section: Introductionmentioning

confidence: 99%

“…In addition, the AE monitoring technique has been widely used in several examples of online monitoring of machining. [11][12][13][14][15][16][17][18][19][20] These publications show that AE monitoring could be a viable method for online monitoring of FMA polishing. Specifically, AE monitoring technology was investigated on charactering abrasive flow machining (AFM), 14 which showed that the AE characteristics of conventional polishing can be reflected by the AE root mean squared (RMS) value.…”

Section: Introductionmentioning

confidence: 99%

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Characterizing acoustic emission signals for the online monitoring of a fluid magnetic abrasives finishing process

Sun

Wang

Longstaff

et al. 2017

Proceedings of the Institution of Mechanical Engineers, Part C:

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Abstract：To implement an automated fluid magnetic abrasive (FMA) finishing process, an online monitoring scheme is proposed based on characterising acoustic emission (AE) signals in this paper. According to the material removal mechanisms during the FMA finishing process, the AE generation and characteristics are predicted analytically to be dominated by the interactions between the surface asperities and the abrasive particles. Moreover, the interactions and corresponding AE events will become weaker as the finishing process progresses and the surface becomes smoother. Experimental studies show that the amplitude and the occurrence rate of continuous AE waves and intermediate bursts

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Characterizing acoustic emission signals for the online monitoring of a fluid magnetic abrasives finishing process

Sun

Wang

Longstaff

et al. 2017

Proceedings of the Institution of Mechanical Engineers, Part C:

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“…The fusion procedure for multiple classifiers [56][57][58] being considered here is illustrated schematically in Figure 2, using the hidden Markov (HMM), Bayesian rule, Gaussian mixture (GMM), and K-means models [59,60].…”

Section: Multi-classifier Fusionmentioning

confidence: 99%

“…A total of 2039 samples were collected, and approximately half of them were used as a training set for the classifiers. Additional details of the experimental set up are provided in[59]. The raw data was then converted to the frequency domain and frequency components were extracted as features.…”

mentioning

confidence: 99%

Monitoring tool wear using classifier fusion

Kannatey‐Asibu

Yum

Kim

2017

Mechanical Systems and Signal Processing

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Real time monitoring of manufacturing processes using a single sensor often poses significant challenge. Sensor fusion has thus been extensively investigated in recent years for process monitoring with significant improvement in performance. This paper presents the results for a monitoring system based on the concept of classifier fusion, and class-weighted voting is investigated to further enhance the system performance. Classifier weights are based on the overall performances of individual classifiers, and majority voting is used in decision making. Acoustic emission monitoring of tool wear during the coroning process is used to illustrate the concept. A classification rate of 87.7 % was obtained for classifier fusion with unity weighting. When weighting was based on overall performance of the respective classifiers, the classification rate improved to 95.6 %. Further using state performance weighting resulted in a 98.5 % classification. Finally, the classifier fusion performance further increased to 99.7% when a penalty vote was applied on the weighting factor.

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“…The tool failure is found to be the most important factor which gives rise to the failure of workpiece. To reduce the negative effects on the workpiece and improve the quality of the products, tool wear condition monitoring (TCM) has been developed based on the usage of various sensors including dynamometer [2], acoustic emission [3,4], and microphone [5]. The application of advanced signal processing techniques such as multiple regression analysis [6], sparse representation [7], and Bayesian-multilayer perceptron [8] has also been studied to produce an accurate monitoring result.…”

Section: Introductionmentioning

confidence: 99%

Multi-scale hybrid HMM for tool wear condition monitoring

Liao

Gao

Lü

et al. 2015

Int J Adv Manuf Technol

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In a machining system, accurate tool wear condition monitoring is paramount for guaranteeing the quality of the workpiece and tool life. Cutting force signal is the most commonly used signal to depict the tool wear variation during the machining process. In this paper, a novel approach for tool wear condition monitoring is proposed, which is based on the multi-scale hybrid hidden Markov model (HHMM) analysis of cutting force signal. The proposed approach captures the deeply mined information of tool wear states and holds an accurate tool wear value monitoring performance from both local and global analyses point of view. The local model deals with the wavelet coefficients of cutting force in different frequencies as a cross-twist Markov depended structure within instant time resolution, which reflects the tool wear state feature from frequency dimension. The global model depicts the long time dynamical degradation of tool wear condition combined with the local model as a composite structure. Experimental studies on CNC turning of nickel alloy, Inconel 718, show that the proposed HHMM approach is efficient in tool wear monitoring and outperforms the single hidden Markov model (HMM).

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A two-step feature selection method for monitoring tool wear and its application to the coroning process

Cited by 8 publications

References 24 publications

Characterizing acoustic emission signals for the online monitoring of a fluid magnetic abrasives finishing process

Characterizing acoustic emission signals for the online monitoring of a fluid magnetic abrasives finishing process

Monitoring tool wear using classifier fusion

Multi-scale hybrid HMM for tool wear condition monitoring

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