Tool Wear Identification in Turning Titanium Alloy Based on SVM

Liao, Zhirong; Li, Sheng Ming; Lü, Yong; Gao, Dong

doi:10.4028/www.scientific.net/msf.800-801.446

Cited by 3 publications

(5 citation statements)

References 8 publications

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“…Datadriven models use accurate mapping between features and wear to solve tool condition monitoring problem. Liao et al [15] proposed a method based on acoustic emission signals using wavelet packet decomposition to extract energy features; a support vector machine (SVM) model was established for TCM. Li et al [16] proposed a time varying and condition adaptive hidden Markov model for capturing tool wear time dependence.…”

Section: Introductionmentioning

confidence: 99%

Tool Wear Prediction Based on Residual Connection and Temporal Networks

Li,

Lei,

You

et al. 2024

Machines

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Since tool wear accumulates in the cutting process, the condition of the cutting tool shows a degradation trend, which ultimately affects the surface quality. Tool wear monitoring and prediction are of significant importance in intelligent manufacturing. The cutting signal shows short-term randomness due to non-uniform materials in the workpiece, making it difficult to accurately monitor tool condition by relying on instantaneous signals. To reduce the impact of transient fluctuations, this paper proposes a novel network based on deep learning to monitor and predict tool wear. Firstly, a CNN model based on residual connection was designed to extract deep features from multi-sensor signals. After that, a temporal model based on an encoder and decoder was built for short-term monitoring and long-term prediction. It captured the instantaneous features and long-term trend features by mining the temporal dependence of the signals. In addition, an encoder and decoder-based temporal model is proposed for smoothing correction to improve the estimation accuracy of the temporal model. To validate the performance of the proposed model, the PHM dataset was used for wear monitoring and prediction and compared with other deep learning models. In addition, CFRP milling experiments were conducted to verify the stability and generalization of the model under different machining conditions. The experimental results show that the model outperformed other deep learning models in terms of MAE, MAPE, and RMSE.

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Section: Introductionmentioning

confidence: 99%

Tool Wear Prediction Based on Residual Connection and Temporal Networks

Li,

Lei,

You

et al. 2024

Machines

View full text Add to dashboard Cite

show abstract

“…Indirect measurement methods can acquire signals in real time through a sensor during tool cutting. After data processing and feature extraction, hidden Markov model (HMM), fuzzy neural network (FNN), back propagation neural network (BPNN), support vector machine (SVM), and other machine learning (ML) models can be used to monitor tool wear [16][17][18]. For example, Zhang Xiang et al proposed micro-milling tool wear identification as the research object and established the HMM of tool wear.…”

Section: Introductionmentioning

confidence: 99%

“…These are used as a classification feature to determine the amount of tool wear. In this method, the SVM is used as the classification method, which can ultimately achieve an accuracy rate of 93.3% [18]. The traditional ML model adopts shallow learning.…”

Section: Introductionmentioning

confidence: 99%

Research on a Real-Time Monitoring Method for the Wear State of a Tool Based on a Convolutional Bidirectional LSTM Model

et al. 2019

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To monitor the tool wear state of computerized numerical control (CNC) machining equipment in real time in a manufacturing workshop, this paper proposes a real-time monitoring method based on a fusion of a convolutional neural network (CNN) and a bidirectional long short-term memory (BiLSTM) network with an attention mechanism (CABLSTM). In this method, the CNN is used to extract deep features from the time-series signal as an input, and then the BiLSTM network with a symmetric structure is constructed to learn the time-series information between the feature vectors. The attention mechanism is introduced to self-adaptively perceive the network weights associated with the classification results of the wear state and distribute the weights reasonably. Finally, the signal features of different weights are sent to a Softmax classifier to classify the tool wear state. In addition, a data acquisition experiment platform is developed with a high-precision CNC milling machine and an acceleration sensor to collect the vibration signals generated during tool processing in real time. The original data are directly fed into the depth neural network of the model for analysis, which avoids the complexity and limitations caused by a manual feature extraction. The experimental results show that, compared with other deep learning neural networks and traditional machine learning network models, the model can predict the tool wear state accurately in real time from original data collected by sensors, and the recognition accuracy and generalization have been improved to a certain extent.

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“…Then, design features are manually extracted from the time domain, the frequency domain, the time-frequency domain, respectively, to reduce the dimensionality. Finally, a hidden Markov model (HMM), neural networks, or support vector machine (SVM) is used for the classification or regression purposes [7][8][9][10][11][12].…”

Section: Introductionmentioning

confidence: 99%

“…Most of the current tool wear detection algorithms collect signals with the indirect methods, which is implemented using machine learning algorithms. For example, Liao et al propose a tool wear condition monitoring system based on the acoustic emission technology [7]. By analyzing representative acoustic signals, the energy ratios from six different frequency bands are selected from the time-frequency domain.…”

Section: Introductionmentioning

confidence: 99%

Research on a Tool Wear Monitoring Algorithm Based on Residual Dense Network

Xie

Huang

et al. 2019

Symmetry

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To accurately and efficiently detect tool wear values during production and processing activities, a new online detection model is proposed called the Residual Dense Network (RDN). The model is created with two main steps: Firstly, the time-domain signals for a cutting tool are obtained (e.g., using acceleration sensors); these signals are processed to denoise and segmented to provide a larger number of uniform samples. This processing helps to improve the robustness of the model. Secondly, a new deep convolutional neural network is proposed to extract features adaptively, by combining the idea of a recursive residual network and a dense network. Notably, this method is specifically tailored to the tool wear value detection problem. In this way, the limitations of traditional manual feature extraction steps can be avoided. The experimental results demonstrate that the proposed method is promising in terms of detection accuracy and speed; it provides a new way to detect tool wear values in practical industrial scenarios.

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Tool Wear Identification in Turning Titanium Alloy Based on SVM

Cited by 3 publications

References 8 publications

Tool Wear Prediction Based on Residual Connection and Temporal Networks

Tool Wear Prediction Based on Residual Connection and Temporal Networks

Research on a Real-Time Monitoring Method for the Wear State of a Tool Based on a Convolutional Bidirectional LSTM Model

Research on a Tool Wear Monitoring Algorithm Based on Residual Dense Network

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