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In metal cutting processing, prediction of the tool wear or VB value can help early warning and timely tool replacement before the tool reaches the service life. Although the deep neural network is an effective method to predict the tool wear, the existing research predicts the tool wear only at its next time moment without considering the tool states at different time points. It ignores important information of the tool wear. In this paper, we propose a comprehensive model, which consists of a monitoring module and a prediction module, to monitor and predict the tool wear for the first time. In the monitoring module, a DenseNet model is constructed to monitor the tool wear via sensor signals. In addition, the prediction module based on attention mechanism is developed by simulating the human brain attention to selectively focus on the important part of processing sequence information. An encoder-decoder structure is introduced for a multi-step prediction of the tool wear. Several VB values in the nearest future are predicted by using sequential VB values monitored in the latest past. Experimental studies show that the short-term information has more influence on the tool wear prediction than the long-term information. The proposed method has been used to predict multi-step VB values in milling operations. [ABSTRACT FROM AUTHOR]

In the interrupted cutting of metal parts, burrs are generally produced at the edge of the exit surface, which will seriously affect the use and assembly performance. Adding deburring process is a way to solve the problem of burr, but it will increase the cost of economy and time, and hinder the automation of production line. This study focused on the active burr control technology, which can prevent burr formation during the cutting process. A new burr-free theoretical model was established based on the analysis of burr formation, and it indicated that changing the exit surface angle of the workpiece was the key to realize burr-free interrupted cutting. The finite element method (FEM) was applied to simulate the cutting burr. In the simulation, ten gradients from 30° to 90° were set for the exit surface angle, both the stress plot and the final burr shape were presented in good agreement with the theoretical model. A cutting experiment of aluminum alloy 7075-T651 was carried out and the burr-free interrupted cutting was realized under the guidance of the derived theoretical model. The critical exit surface angle of burr-free state was also verified by the experiment, which was predicted from the theoretical model. This study provided a theoretical and practical basis for solving burr problem in interrupted cutting. [ABSTRACT FROM AUTHOR]

The tension–compression asymmetry of metallic glasses has shown that the Mohr–Coulomb yield criterion can better determine the fracture characteristics of metallic glasses than the traditional Tresca and Von Mises criterion. Available cutting models based on the traditional yield criterion cannot describe the cutting mechanism of metallic glasses. An orthogonal cutting mechanics model of metallic glasses based on the Mohr–Coulomb yield criterion taking into account the influence of normal stress and temperature on shear plane is established. The analytical expressions for cutting force, shear angle and average temperature rise on shear plane are derived. Orthogonal turning experiments were designed and carried to validate the developed model. The analytically calculated cutting forces from the developed model considering cutting temperature rise were compared with the measured forces and the results show that a small difference of 7.7% was found, which is contrary to the prediction from traditional model based on von Mises yield criterion (nearly 60%) and the prediction from the model based on M–C without considering cutting temperature rise (more than 70%). The comprehensive comparison results show that the developed analytical model can accurately predict cutting process for metallic glasses. [ABSTRACT FROM AUTHOR]