关键词: 机械制造工艺与设备, 低速单向走丝电火花线切割, TC4, 三维表面粗糙度, 响应曲面, BP神经网络

Abstract: Low speed wire electrical discharge machining (LS-WEDM) has an irreplaceable role in titanium alloy machining, but the complexity of microcosmic discharge makes it difficult to build the surface roughness model. Furthermore, the existing machine systems can not provide any machining parameters of titanium alloy. Box-Behnken experiment of TC4 as research object is designed by Design-Expert software. The three-dimensional contourgraph and scanning electron microscope are used to analyze the surface morphology, power spectrum and re-solidified layer, and the finished surface is isotropic and different from grinding surface. The observed result shows that the open circuit voltage is 100 V with the peak current of 40 A, and the crack extends to TC4 matrix with the pulse width of 18 μs. A quadratic model of three-dimensional surface roughness is established after model selection and significance test by using response surface method. In order to improve the prediction accuracy and generalization ability, BP neural network is applied to build a combined model. The verification experiments show that it can reduce the mean absolute error from 4.33% to 3.26% for in-sample and from 13.31% to 8.50% for out-sample, which provides a new method for electrical discharge machining simulation.

Key words: manufacturing technology and equipment, low speed wire electrical discharge machining, TC4, three-dimensional surface roughness, response surface, BP neural network