Prediction of Machining Dimension in Laser Heating and Ultrasonic Vibration Composite Assisted Cutting of Tungsten Carbide

Abstract

Dimension precision plays an important role in precision machining. The two-dimensional ultrasonic vibration cutting (TDUVC) method reduces cutting force and alleviates tool wear, meanwhile, laser assisted cutting (LAC) improves the material workability under high temperature. In this paper, laser heating and two-dimensional ultrasonic vibration were combined in cutting of tungsten carbide (YG10) to improve machining dimension precision. According to the experimental results, a prediction model of machining dimension was built based on time series model. The results show that the machining dimension precision is improved significantly in laser and ultrasonic composite assisted cutting (LUAC), and AR (2) and AR (12) of time series model predicts machining dimension with high precision (the relative error is less than 10%), and reflects tool wear state. Moreover, comparison with artificial neural network (ANN) also proves that the time series model is more suitable for the prediction of machining dimensional in LUAC.