Machine learning approach for prediction of hole oversize using acoustic emission signal features in ultrasonic machining of inconel 718

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ABSTRACT The material removal in the ultrasonic machining process is due to the fracture of the workpiece material. The fracture is owing to the energy transfer by vibrating abrasive particles. The vibrating particles impact the workpiece surface and machining takes place, leading to the size of the machining hole, which is of interest to understand to guarantee efficient machining with accuracy and precision. In the current investigation, the hole oversize (HOS) 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 domain. The features and process parameters are input to a support vector regression model to estimate HOS. The model developed for HOS prediction yields an accuracy of 97.87%. The developed model can be beneficial for the real-time monitoring of HOS during the ultrasonic machining process for industrial and remote applications.