Abstract
Accurate monitoring and prediction of tool wear conditions have an important influence on the cutting performance, thereby improving the machining precision of the workpiece and reducing the production cost. However, traditional methods cannot easily achieve exact supervision in real time because of the complexity and time-varying nature of the cutting process. A method based on Digital Twin (DT), which establish a symmetrical virtual tool system matching exactly the actual tool system, is presented herein to realize high precision in monitoring and predicting tool wear. Firstly, the framework of the cutting tool system DT is designed, and the components and operations rationale of the framework are detailed. Secondly, the key enabling technologies of the framework are elaborated. In terms of the cutting mechanism, a virtual cutting tool model is built to simulate the cutting process. The modifications and data fusion of the model are carried out to keep the symmetry between physical and virtual systems. Tool wear classification and prediction are presented based on the hybrid-driven method. With the technologies, the physical–virtual symmetry of the DT model is achieved to mapping the real-time status of tool wear accurately. Finally, a case study of the turning process is presented to verify the feasibility of the framework.
Highlights
Cutting tools, as key components of the Computer Numerical Control Machine Tool (CNCMT), significantly affect the quality of machined products and the safety of the CNCMT
The tool edge and flank are generally covered by the chip and workpiece; as such, tool wear images cannot be obtained during the cutting process
In the cutting tool service system, a hybrid-driven model is built: firstly, all raw data from the physical and virtual systems are processed to eliminate noise and interference signals; secondly, the virtual system is modified by comparing the error between the physical and virtual systems, thereby realizing the evolution of the Digital Twin (DT) model; the tool wear status is predicted based on the extracted data with the help of machine learning algorithms
Summary
As key components of the Computer Numerical Control Machine Tool (CNCMT), significantly affect the quality of machined products and the safety of the CNCMT. If manufacturers can obtain the real-time tool wear condition and change the cutting tool within the wear standard, the production efficiency and workpiece performance can be improved significantly. Considering that the chip and workpiece hide the flank and tool edge, the cutting process must be paused to capture images of cutting tools; this increases the downtime of the CNCMT and decreases the processing efficiency Another typically used method is indirect measurement, which realizes tool wear monitoring and prediction by extracting the signals of the cutting process, such as the cutting force, vibration, acoustic emission signals, etc. In terms of the cutting mechanism, a virtual cutting tool model is built to simulate the cutting process, mapping the physical space accurately in real time. Within the framework of the DT method, tool wear monitoring and prediction can be achieved with high accuracy over time.
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