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Most of existing researches related to ultrasonic machining on material removal mechanism are focused on single-grainultrasonic scratch and undeformed chip thickness. In this study for the rotary ultrasonic face milling (RUFM), two material removal modes of single and its adjacent abrasive grain with spindle speed were investigated. The impact and scratch with high-frequency vibration has been illustrated by the kinematic of multi-grain movement. We conducted the RUFM experiment to verify the proposed material removal modes. Experimental result shows that the high-frequency impact can increase surface roughness as undeformed chip thicknessdecrease, and high-frequency scratch can reduce the surface roughness as undeformed chip thickness turns up.

Ultrasonic machining is a hybrid machining process which combination of the additional rotary and ultrasonic vibration energy. It has been successfully applied in hard and brittle materials processing. In the ultrasonic processing application, the vibration energy will be different in the same driving current under the conditions of different parameters of the transducer. This paper proposes a method to predict the driving current using Elman neural network. According to the different parameters of the transducer, the different driving current required by the same vibration energy is obtained by the Elman neural network prediction model. A number of experimental data have been collected to be used by training the network model. Training results and predictive results show that it can be well adjusted by the driving current to obtain the same vibration energy and further to get the stable ultrasonic energy.