Parameter Optimization of a Five-Axis Tool Grinder Using Grey Relational Analysis
This paper uses the grey relational analysis to find the optimal values of parameters of the servo drives and the controller of a five-axis CNC tool grinder in order to improve precision of grinding and accuracy of end mills. The experimental planning and design are based on the Taguchi method. There are totally six control factors in the experiments, and each factor has three levels. An L18 orthogonal array was applied for the experiments, and each experiment was repeated three times. The grey relational approach was then employed to find the optimal values to the drives and the controller. These values were utilized for grinding a ball nose end mill of cemented tungsten carbide with two-flute and 6 mm in outside diameter. Finally, a well-known tool measuring and inspection machine was used to measure the geometric parameters of the end mill for the initial design and the optimal design. Experimental results show that the grinding time is reduced up to 6.02 ％, and the precision of the ball nose end mill is also improved. Thus, the results demonstrate the effectiveness of the proposed approach.
Hun Guo, Zuo Dunwen, Hongli Xu, Chun Su, Chunjie Liu and Weidong Jin
J. Y. Chen and B. Y. Lee, "Parameter Optimization of a Five-Axis Tool Grinder Using Grey Relational Analysis", Key Engineering Materials, Vol. 458, pp. 246-251, 2011