Stiffness modeling of machine tools based on machining space analysis

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• 作者：Xianming Gao ; Baotong Li ; Jun Hong…
• 关键词：Machine tool ; Stiffness modeling ; Machining space ; Transfer ; matrix method ; Error prediction
• 刊名：The International Journal of Advanced Manufacturing Technology
• 出版年：2016
• 出版时间：September 2016
• 年：2016
• 卷：86
• 期：5-8
• 页码：2093-2106
• 全文大小：2,102 KB
• 刊物类别：Engineering
• 刊物主题：Industrial and Production Engineering
  Production and Logistics
  Mechanical Engineering
  Computer-Aided Engineering and Design
  
• 出版者：Springer London
• ISSN：1433-3015
• 卷排序：86

文摘

Machining stiffness, which serves the overall stability of a machine tool, plays a significant role in determining the resulting machining errors in a machine tool. Therefore, the machine tools can thus be improved to get high levels of precisions directly from what we behold as the most important determinant, the overall stiffness. In this paper, a novel approach modeling the static stiffness field of the machining space is being discussed. Within this method, a parametric model, considering six-directional static stiffness, is established to design and evaluate the static stiffness model of a high-performance computerized numerical control (CNC) machine tool. The result is then used for further discussions on the estimating and reducing of the machining errors. The machining errors are predicted by using the same CNC model under various load bearings considering the practical machining positions and stress conditions. Moreover, the effect of the six-directional static stiffness on the machining errors according to each position in the machining space is researched, where we investigate the possible solutions to reduce the machining errors with the processing technology put into consideration. In the end, by experimenting on boring-milling machining center, we verify the accuracy and efficiency of the proposed error prediction method by comparing the results from experimental data and those from finite element analysis. The proposed model is then proved to have great efficiency in optimizing the machine tool’s layout and structural design based. This improved modeling strongly suggests a favorable application for future manufacturing processes of machine tools.