Papers by Author: Jing Zhu Pang

Abstract: In order to reconcile the contradiction between the ceramic grinding efficiency and surface integrity, high-speed grinding with diamond grinding wheel is supposed to be a solution. In this paper, first of all, a normal grinding force model is proposed based on the consideration of the material property and the grinding process parameters. It can be seen that an elevated grinding wheel velocity in combination of a higher workpiece speed can increase the machining rate while maintaining the desired surface integrity. After a series of grinding tests, a comprehensive measurement has been done to study the surface damage by the surface roughness, the microscope profile and the X-ray diffraction. In particular, the effect of the grinding parameters on the ground surface are analyzed and reported.

Abstract: This paper presents the effects of spindle system configuration on the dynamic and static characteristics of high speed grinding. A 3D physical mode of high-speed grinding motorized spindle system with rotation speed of 150m/s was provided. The motorized spindle system consists of bearings, rotor, stator, spindle housing and grinding wheel. Based on the finite element method (FEM), the static characteristics, dynamic and the transient response are analyzed based on the finite element analysis software NASTRAN. It is shown that the spindle overhanging, bearing span have a significant effort on spindle deflection. The dynamic analysis shows no resonance will happen during its speed range. The methods and solutions for the motorized spindle system design and engineering applications was given in this paper.

Abstract: Grinding is widely used as a precision process for machining difficult-to-cut materials. Grinding productivity is still greatly dependent on the experience and skill of human operators. Focusing on the indirect method, an attempt was made to build up an intelligent system to monitor the condition of grinding wheels with force signals and the acoustic emission (AE) signals. An artificial immune algorithm based multi-signals processing method was presented in this paper. The intelligent system is capable of incremental supervised learning of grinding conditions and quickly pattern recognition, and can continually improve the monitoring precision. The experiment indicates that the accuracy of condition identification is about 87%, and able to meet the industrial need on the whole.