Machine learning coupled with acoustic emission signal features for tool wear estimation during ultrasonic machining of Inconel 718

Abstract

The material removal in the ultrasonic machining process is due to the fracture of the workpiece material. The fracture is due to energy transfer by vibrating abrasive particles. The vibration energy is induced by a tool oscillating at a frequency of more than 20 kHz. The vibrating particles not only impact the workpiece surface but also impact the oscillating tool, and it leads to the fracture of the tool, which is of interest to understand to guarantee efficient machining with accuracy and precision. In the current investigation, the tool wear during the ultrasonic machining of Inconel 718 super alloy is attempted. An acoustic emission sensor is integrated with the machining setup, and signal information is extracted in the time and time-frequency domains. The features and process parameters are input to a support vector regression model to estimate tool wear. The model developed for tool wear prediction yields an accuracy of 96.13% compared to the model developed with only process parameters, which delivers an accuracy of 84.89%. The developed model can be beneficial for the real-time monitoring of tool wear during the ultrasonic machining process for industrial and remote applications.