Real-time tool condition monitoring method based on in situ temperature measurement and artificial neural network in turning

Tool failures in machining processes often cause severe damages of workpieces and lead to large quantities of loss, making tool condition monitoring an important, urgent issue. However, problems such as practicability still remain in actual machining Here, a real-time tool condition monitoring method integrated in an in situ fiber optic temperature measuring apparatus is proposed. A thermal simulation is conducted to investigate how the fluctuating cutting heats affect the measuring temperatures, and an intermittent cutting experiment is carried out, verifying that the apparatus can capture the rapid but slight temperature undulations. Fourier transform is carried out. The spectrum features are then selected and input into the artificial neural network for classification, and a caution is given if the tool is worn. A learning rate adaption algorithm is introduced, greatly reducing the dependence on initial parameters, making training convenient and flexible. The accuracy stays 90% and higher in variable argument processes. Furthermore, an application program with a graphical user interface is constructed to present real-time results, confirming the practicality.

Automated summary

Tool condition monitoring is an important issue in machining processes, but practical problems still exist. In this study, a real-time tool condition monitoring method integrated with an in situ fiber optic temperature measuring apparatus is proposed. Thermal simulation and intermittent cutting experiments are conducted to verify the effectiveness of this method. Fourier transform and artificial neural network are used for classification, and a learning rate adaption algorithm is introduced to improve accuracy and flexibility in training. An application program with graphical user interface is constructed to present real-time results and confirm the practicality.