Development of empirical models for the prediction of surface functional parameters in ultrasonic elliptical vibration cutting

Over the past decades, significant improvement has been witnessed in using 3D surface parameter characterization to study machined surfaces’ functional properties. However, the prediction and optimization of these valuable metrics under the influence of various cutting conditions have remained a significant concern. This paper focuses on modeling the effects of cutting speed, feed rate, depth of cut, and vibration amplitude on machining output responses in terms of 3D surface functional parameters, including surface bearing index (Sbi) and core fluid retention index (Sci). Ultrasonic elliptical vibration cutting (UEVC) experiments are performed to evaluate the variation trend of surface functional parameters while adopting the Taguchi analysis and response surface methodology (RSM) approach. The analysis results revealed that the cutting speed has the most significant impact on both (Sbi) and (Sci), followed by feed rate and vibration amplitude in varying rankings between responses; and lastly, the depth of cut, which exhibited the least effect. The validation test result indicated that the developed RSM predictive model was adequate in construction and verification. Finally, the output variables are optimized through the desirability function (CDF) approach. A comparative experimental analysis between conventional cutting (CC) and UEVC was performed under the optimized parameter setting. The comparison result revealed that the functional indices were substantially enhanced under the UEVC method.