The Research On Titanium Alloy Grinding Wheel Wear And Surface Roughness Of The Workpiece

Titanium alloy is widely used in various industries due to its good material properties such as high specific strength, good corrosion resistance and so on. However, wheel adhesion is easy to be happened in the grinding process because of the low thermal conductivity, high chemical activity and other characteristics of titanium alloy,which will cause grinding wheel’s wear, grinding vibration, even the burned work-surface. In order to improve surface quality of Titanium alloy during the grinding process in plain cylindrical grinder with a middle-low grinding speed, the dissertation mainly focused on the cylindrical grinding technology of TC4 Titanium alloy, on-line monitoring system for grinding wheel’s wear and prediction of the surface roughness based on grinding vibration signal and grinding parameters and carried the following research:The paper analyzed properties of Titanium alloy material first and chose TC4 as subsequent experimental study object for its good machinability. The comparison between processing property in high-speed and middle-low grinding of Titanium alloy was executed. For middle-low grinding of Titanium alloy, which has a lower production cost and wider using range, the method that takes advantage of vibration signal for the monitoring of grinding wheel’s wear and the surface roughness to guarantee the machining quality of Titanium alloy was put forward. On the basis, the paper designed the vibration signal acquisition and analysis system of Titanium alloy grinding, including the selection and arrangement of transducers, the hardware configuration of data acquisition system and software development covered acquisition parameter settings, feature selection and pretreatment to vibration signal and vibration signal analysis method.Based on the acquisition and analysis system of grinding vibration signal, the paper did a research on grinding wheel’s wear state in normal external cylindrical grinding of Titanium alloy. Through the analysis of experiment results, the RMS of vibration signal was chose and the distinguishing process of grinding wheel wear was established. With the discrimination threshold determined, the on-line monitor software of grinding wheel’s wear was developed. By actual grinding of Titanium alloy, the system was proved to be effective and it can remind the severe wear of grinding wheel accurately.Grinding wheel wear decides surface quality of the titanium alloy directly. However, the surface roundness can’t be achieved directly during the grinding. Consequently, the vibration signal was chose to reveal the surface roundness. Firstly, a orthogonal experiment was executed to research how different grinding parameters and different degrees of grinding wheel wear influence the vibration signal and surface roundness. And then we did an analysis on mapping relationship between vibration signal and surface roundness.By synthesizing the research results above, a prediction model for surface roundness of Titanium alloy was build using BP neural network, which takes vibration signal, grinding depth and rotating speed of workpiece as input. The structure design and training process of the BP neural network was introduced in detail. The result of experiment has shown that this prediction model is effective for surface roundness of TC4 Titanium alloy in its middle-low cylindrical grinding process.