Deep learning-based tool wear prediction and its application for machining process using multi-scale feature fusion and channel attention mechanism

Xu, Xingwei; Wang, Jianwen; Zhong, Bingfu; Ming, Weiwei; Chen, Ming

doi:10.1016/j.measurement.2021.109254

Cited by 90 publications

(22 citation statements)

References 40 publications

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“…Certain methods are introduced to ensure that the input features are highly related to the tool state, which is helpful for building models with lower computation costs or reducing overfitting problems. Finally, with increasing knowledge of artificial intelligence, machine learning techniques have been used to achieve pattern recognition for the tool wear state and have presented a good performance [19][20][21][22][23][24]. However, the process of generating tool wear and machining is complicated, which simultaneously affects several phenomena and leads to the sudden malfunction of sensors in monitoring.…”

Section: Introductionmentioning

confidence: 99%

Robust tool wear monitoring system development by sensors and feature fusion

Lin

Lee

2022

Asian Journal of Control

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This study introduces a tool wear monitoring system that uses multiple sensors and a feature fusion technique. To improve the robustness of the system, different tightening torque and spindle speed conditions were considered in the experimental design stage. Vibration signals in three coordinates and sound signals were collected and transformed by fast Fourier transform for feature extraction. Two types of features in the frequency domain were used to establish the proposed system, including the mean value features selected by the class mean scatter feature selection criterion and statistical features by Gradient Class Activation Mapping++ (Grad-CAM++). The proposed monitoring system was established using a hierarchical neural network structure and feature fusion of the sensors. Cross-validation was introduced to demonstrate the performance and effectiveness of our approach under various tightening torque values and spindle speeds.

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Section: Introductionmentioning

confidence: 99%

Robust tool wear monitoring system development by sensors and feature fusion

Lin

Lee

2022

Asian Journal of Control

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“…The experimental results showed that the method achieved accurate remaining useful life prediction of the turbofan engine. Xu et al 27 introduced the channel attention mechanism into the deep learning model based on CNN and considered the weight of different feature map to enhance the performance of the model. Achievements have been made in the research above.…”

Section: Introductionmentioning

confidence: 99%

Local-feature and global-dependency based tool wear prediction using deep learning

Yang

Zhou

et al. 2022

Sci Rep

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Evaluation of tool wear is vital in manufacturing system, since early detections on worn-out condition can ensure workpiece quality, improve machining efficiency. With the development of intelligent manufacturing, tool wear prediction technology plays an increasingly important role. However, traditional tool wear prediction methods rely on experience and knowledge of experts and are labor-extensive. Deep learning provides an effective way to extract features of raw data and establish the mapping relationship between features and targets automatically. In this paper, a new local-feature and global-dependency based tool wear prediction method is proposed. It is a hybrid approach combining manual features with automatic features. Firstly, an enhanced CNN network is designed and applied on the transformed wavelet scalogram to learn the local single-scale specific features and multi-scale correlation features automatically. Secondly, sequence of local feature vectors combining manual features with automatic features are fed into multi-layer LSTM step by step for the global dependency. A fully connected layer is then trained to predict tool wear. Finally, two statistics are proposed to illustrate the overall prediction performance and generalization ability of the model. An experiment illustrates the effectiveness of our proposed method under multiple working conditions.

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“…Additionally, deep learning models such as convolutional neural network (CNN) [23] and long short-term memory (LSTM) [24][25] are also widely used in the tool RUL prediction.…”

Section: Introductionmentioning

confidence: 99%

A hybrid remaining useful life prediction method for cutting tool considering the wear state

Xiang

Pan

et al. 2022

Int J Adv Manuf Technol

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Accurate cutting tool remaining useful life (RUL) prediction is of significance to guarantee the cutting quality and minimize the production cost. Recently, physics-based and data-driven methods have been widely used in the tool RUL prediction. The physicsbased approaches may not accurately describe the time-varying wear process due to a lack of knowledge for underlying physics and simplifications involved in physical models, while the data-driven methods may be easily affected by the quantity and quality of data.To overcome the drawbacks of these two approaches, a hybrid prognostics framework considering tool wear state is developed to achieve an accurate prediction. Firstly, the mapping relationship between the sensor signal and tool wear is established by support vector regression (SVR). Then, the tool wear statuses are recognized by support vector machine (SVM) and the results are put into a Bayesian framework as prior information.Thirdly, based on the constructed Bayesian framework, parameters of the tool wear model are updated iteratively by the sliding time window and particle filter algorithm. Finally, the tool wear state space and RUL can be predicted accordingly using the updating tool wear model. The validity of the proposed method is demonstrated by a high-speed machine tool experiment. The results show that the presented approach can effectively reduce the uncertainty of tool wear state estimation and improve the accuracy of RUL prediction.

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Deep learning-based tool wear prediction and its application for machining process using multi-scale feature fusion and channel attention mechanism

Cited by 90 publications

References 40 publications

Robust tool wear monitoring system development by sensors and feature fusion

Robust tool wear monitoring system development by sensors and feature fusion

Local-feature and global-dependency based tool wear prediction using deep learning

A hybrid remaining useful life prediction method for cutting tool considering the wear state

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