Application of the Empirical Mode Decomposition method for the prediction of the tool wear in turning operation

Khemissi, Babouri Mohamed; Ouelaa, Nouredine; Djebala, Abderrazek

doi:10.5755/j01.mech.23.2.8971

Cited by 7 publications

(2 citation statements)

References 10 publications

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“…Chen et al [13] used Wavelet Packet Transform (WTP) method to decompose the original signals into different frequency bands, features extracted from these frequency bands were ranked via correlation analysis, after the salient features were selected, a logistic regression model was established for reliability estimation. Babouri et al [14] reported that the energy and the mean power of the first Intrinsic Modes Functions (IMF) of the EMD shows great potential in tool wear status recognition and the method is validated by a turning process. Chang et al [15] developed a tool wear status identification method based on the optimize VMD, and the informative IMF was selected as the sensitive IMF components, in which the relationship between features and tool wear degree was built by the Naïve Bayes classifier method.…”

Section: Introductionmentioning

confidence: 99%

Tool condition monitoring for cavity milling based on bispectrum analysis and Bayesian optimized SVM

Li¹,

Wang

Hu³

et al. 2023

Preprint

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Tool wear status seriously affects the dimensional accuracy and surface quality of the machined parts. Therefore, tool condition monitoring (TCM) is essential in the milling process of aerospace structural parts due to the use of difficult-to-cut materials and complex cutting trajectories. The Higher order spectrum (HOS) was first employed to analyze the vibration signals, and then bispectral features extracted from de-noised signals were used to characterize the tool wear status. The improved Pearson’s correlation coefficient was used for feature selection to reduce the influence of periodic components on feature selection process. Furthermore, a novel objective function was proposed to guide the hyperparameters optimization process of support vector machine based on Bayesian optimization algorithm, in which the effect of imbalanced data on the recognition rates was considered. To demonstrate the effectiveness of the proposed method, a structural part milling experiment was performed on a vertical machining center and vibration signals of spindle were collected. Based on this, an online TCM model was established. The present study suggests that the proposed TCM system is accurate and robust.

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Section: Introductionmentioning

confidence: 99%

Tool condition monitoring for cavity milling based on bispectrum analysis and Bayesian optimized SVM

Li¹,

Wang

Hu³

et al. 2023

Preprint

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“…As prediction accuracy largely depends on the quality of collected sensor data, it is recommended to reduce noise before using the collected data as inputs in a data-driven method (Dey and Yodo, 2020). A variety of denoising techniques has been primarily used for this purpose; for instance, Khemissi et al (2017) and Dey and Yodo (2021) used empirical mode decomposition (EMD), Zhang et al (2015) applied the Wavelet denoising technique, and Wu et al (2018) applied ensemble EMD (EEMD).…”

Section: Introductionmentioning

confidence: 99%

Performance Improvement Techniques for Neural Networks in Tool Condition Monitoring

Yodo¹,

Dey²

2022

IJMMS

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The performance of data-driven algorithms in tool condition monitoring often depends on the combinations of different factors such as data quality, input dimensions, and model architecture. Several performance improvement techniques such as data denoising, feature selection, and regularisation techniques are known to enhance prediction accuracy. Moreover, selecting model architecture and tuning hyperparameters also significantly impact the prediction performance. Although the prediction accuracy of a data-driven method can be improved using these techniques, their importance is rarely discussed for tool condition monitoring. In this paper, the importance of various performance improvement techniques is extensively analysed by applying them to a CNC milling machine dataset for tool wear prediction. The investigation results and performance measurement metrics showed data denoising techniques, feature reduction techniques, and regularisation methods improved prediction accuracy up to around 55%. The selection of techniques for improving the accuracy depends on the nature of a dataset and applied algorithms.

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Tool condition monitoring for cavity milling based on bispectrum analysis and Bayesian optimized SVM

Li,

Wang,

et al. 2024

Int J Adv Manuf Technol

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Application of the Empirical Mode Decomposition method for the prediction of the tool wear in turning operation

Cited by 7 publications

References 10 publications

Tool condition monitoring for cavity milling based on bispectrum analysis and Bayesian optimized SVM

Tool condition monitoring for cavity milling based on bispectrum analysis and Bayesian optimized SVM

Performance Improvement Techniques for Neural Networks in Tool Condition Monitoring

Tool condition monitoring for cavity milling based on bispectrum analysis and Bayesian optimized SVM

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