A Study on Tool Wear Prediction in Ultrasonic Vibration Turning of Tungsten Carbide

2016 ◽  
Vol 693 ◽  
pp. 1228-1234 ◽  
Author(s):  
Feng Jiao ◽  
Ying Niu ◽  
Jia Liang Qi ◽  
Jie Li
Keyword(s):  
Time Series ◽  
Tool Wear ◽  
Tungsten Carbide ◽  
Ultrasonic Vibration ◽  
Flank Wear ◽  
Machining Process ◽  
Two Dimensional ◽  
Wear Prediction ◽  
Tool Wear Prediction ◽  
Ultrasonic Cutting

The prediction of tool wear can help understand the influence of tool wear on the machining process and result, and change or grind the worn tool in time. The two-dimensional ultrasonic vibration turning method can reduce the crack of tool and decrease the negative effect on processing thus extends the tool life. In this paper, two-dimensional ultrasonic cutting theory was applied to the precision machining of tungsten carbide. With self-developed two-dimensional ultrasonic cutting device, series of cutting experiments were carried out. During cutting process, the flank wear under different cutting length was observed; flank wear situations were compared with those in traditional cutting. In order to predict the tool wear and thus heighten the machining precision, a tool wear prediction model based on time series analysis method was built in the paper. The research results show the built AR (9) time series model can predict the flank wear condition with high precision.

Prediction of Machining Dimension in Laser Heating and Ultrasonic Vibration Composite Assisted Cutting of Tungsten Carbide

2018 ◽  
Vol 17 (01) ◽  
pp. 35-45 ◽  
Author(s):  
Feng Jiao ◽  
Ying Niu ◽  
Ming-Jun Zhang
Keyword(s):  
Time Series ◽  
Tool Wear ◽  
Tungsten Carbide ◽  
Ultrasonic Vibration ◽  
Laser Heating ◽  
Time Series Model ◽  
Two Dimensional ◽  
Dimension Precision ◽  
Artificial Neural Network Ann ◽  
Ultrasonic Vibration Cutting

Dimension precision plays an important role in precision machining. The two-dimensional ultrasonic vibration cutting (TDUVC) method reduces cutting force and alleviates tool wear, meanwhile, laser assisted cutting (LAC) improves the material workability under high temperature. In this paper, laser heating and two-dimensional ultrasonic vibration were combined in cutting of tungsten carbide (YG10) to improve machining dimension precision. According to the experimental results, a prediction model of machining dimension was built based on time series model. The results show that the machining dimension precision is improved significantly in laser and ultrasonic composite assisted cutting (LUAC), and AR (2) and AR (12) of time series model predicts machining dimension with high precision (the relative error is less than 10%), and reflects tool wear state. Moreover, comparison with artificial neural network (ANN) also proves that the time series model is more suitable for the prediction of machining dimensional in LUAC.

scholarly journals Comparison of various tool wear prediction methods during end milling of metal matrix composite

2018 ◽  
Vol 38 (1) ◽  
pp. 1-7
Author(s):  
Martyna Wiciak ◽  
Paweł Twardowski ◽  
Szymon Wojciechowski
Keyword(s):  
Tool Wear ◽  
Metal Matrix Composite ◽  
Matrix Composite ◽  
Metal Matrix ◽  
Regression Models ◽  
Flank Wear ◽  
End Milling ◽  
Wear Prediction ◽  
Diagnostic Measures ◽  
Tool Wear Prediction

Abstract In this paper, the problem of tool wear prediction during milling of hard-to-cut metal matrix composite Duralcan™ was presented. The conducted research involved the measurements of acceleration of vibrations during milling with constant cutting conditions, and evaluation of the flank wear. Subsequently, the analysis of vibrations in time and frequency domain, as well as the correlation of the obtained measures with the tool wear values were conducted. The validation of tool wear diagnosis in relation to selected diagnostic measures was carried out with the use of one variable and two variables regression models, as well as with the application of artificial neural networks (ANN). The comparative analysis of the obtained results enable.

scholarly journals Multi-Scale Convolutional Gated Recurrent Unit Networks for Tool Wear Prediction in Smart Manufacturing

2021 ◽  
Vol 34 (1) ◽  
Author(s):  
Weixin Xu ◽  
Huihui Miao ◽  
Zhibin Zhao ◽  
Jinxin Liu ◽  
Chuang Sun ◽  
...  
Keyword(s):  
Tool Wear ◽  
Information Technologies ◽  
Manufacturing Systems ◽  
Smart Manufacturing ◽  
Wear Prediction ◽  
Multi Scale ◽  
Tool Wear Prediction ◽  
Machine Health Monitoring ◽  
Gated Recurrent Unit ◽  
Unit Network

AbstractAs an integrated application of modern information technologies and artificial intelligence, Prognostic and Health Management (PHM) is important for machine health monitoring. Prediction of tool wear is one of the symbolic applications of PHM technology in modern manufacturing systems and industry. In this paper, a multi-scale Convolutional Gated Recurrent Unit network (MCGRU) is proposed to address raw sensory data for tool wear prediction. At the bottom of MCGRU, six parallel and independent branches with different kernel sizes are designed to form a multi-scale convolutional neural network, which augments the adaptability to features of different time scales. These features of different scales extracted from raw data are then fed into a Deep Gated Recurrent Unit network to capture long-term dependencies and learn significant representations. At the top of the MCGRU, a fully connected layer and a regression layer are built for cutting tool wear prediction. Two case studies are performed to verify the capability and effectiveness of the proposed MCGRU network and results show that MCGRU outperforms several state-of-the-art baseline models.

Research on tool wear prediction based on temperature signals and deep learning

Wear ◽  
2021 ◽  
pp. 203902
Author(s):  
Zhaopeng He ◽  
Tielin Shi ◽  
Jianping Xuan ◽  
Tianxiang Li
Keyword(s):  
Deep Learning ◽  
Tool Wear ◽  
Wear Prediction ◽  
Tool Wear Prediction
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