Tool wear classification based on maximal overlap discrete wavelet transform and hybrid deep learning model

Abdeltawab, Ahmed; Xi, Zhang; longjia, Zhang

doi:10.1007/s00170-023-12797-w

Cited by 5 publications

(1 citation statement)

References 57 publications

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“…Liu et al [ 26 ] developed an intelligent tool wear monitoring system, containing a multi-input parallel convolutional network to select and preprocess the multi-scale degradation features, and a long short-term memory (LSTM) model was established to achieve the estimation of the tool wear. Abdeltawab et al [ 27 ] divided the tool wear status into five stages and established a CNN-LSTM model to achieve the online identification of wear stage.…”

Section: Introductionmentioning

confidence: 99%

An Advanced Tool Wear Forecasting Technique with Uncertainty Quantification Using Bayesian Inference and Support Vector Regression

Rong,

Li,

et al. 2024

Sensors

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Tool wear prediction is of great significance in industrial production. Current tool wear prediction methods mainly rely on the indirect estimation of machine learning, which focuses more on estimating the current tool wear state and lacks effective quantification of random uncertainty factors. To overcome these shortcomings, this paper proposes a novel method for predicting cutting tool wear. In the offline phase, the multiple degradation features were modeled using the Brownian motion stochastic process and a SVR model was trained for mapping the features and the tool wear values. In the online phase, the Bayesian inference was used to update the random parameters of the feature degradation model, and the future trend of the features was estimated using simulation samples. The estimation results were input into the SVR model to achieve in-advance prediction of the cutting tool wear in the form of distribution densities. An experimental tool wear dataset was used to verify the effectiveness of the proposed method. The results demonstrate that the method shows superiority in prediction accuracy and stability.

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Section: Introductionmentioning

confidence: 99%