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A novel prediction method of machining accuracy for machine tools based on tolerance

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Abstract

The ongoing research is hard to predict the machining accuracy of machine tools in the preliminary design phase. Hence, a rapid and quantitative prediction method seems very necessary and imperative. In this study, a new machining accuracy prediction strategy for a five-axis machine tool (FAMT) based on tolerance is proposed. Firstly, a characterization model of surface error profiles for key parts of the FAMT is established, and GESP prediction models based on tolerance for a translational axis and a rotary axis are established by utilizing the mapping relationship between tolerance, surface error profiles, and GESPs of key parts for the FAMT. Then, the kinematic equation of the FAMT is established based on GESPs by using the multi-body system (MBS) theory. As a basis, the actual machining position for the cone frustum based on GESPs by considering the tool setting position and the relative NC machining instruction are solved. And, a machining accuracy prediction model for the cone frustum based on tolerance is established. To enhance the prediction efficiency and intuitively evaluate the machining performance of the FAMT, some accuracy indexes of the cone frustum, such as roundness, angularity, and concentricity, are described. Thus, the machining accuracy prediction model based on key parts’ tolerance parameters of the FAMT is established substantially. Finally, a simulation prediction scheme is conducted. Simulation results show that predicted machining accuracy can meet the requirements of accuracy. To further demonstrate the practicability of the method, experimental verification is implemented. Experimental results show measuring values are very close to predictive ones. Therefore, a reasonable conclusion can be drawn that the proposed method can quickly and effectively predict the machining accuracy of machine tools based on tolerance.

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Funding

This work is financially supported by the Key Science and Technology Research Project of the Henan Province, China (Nos. 202102210086 and 202102210289); the Key Scientific Research Project for Henan Province Higher school of China (No. 18A460036); the National Natural Science Foundation of China (No. 51775010); and the Doctoral Research Fund Project of the Zhengzhou University of Light Industry for Henan Province of China.

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Authors and Affiliations

  1. Henan Key Laboratory of Intelligent Manufacturing of Mechanical Equipment, Zhengzhou University of Light Industry, Zhengzhou City, 450002, Henan Province, China

    Changjun Wu & Qiaohua Wang

  2. College of Mechanical Engineering & Applied Electronic Technology, Beijing University of Technology, Beijing, 100124, China

    Jinwei Fan & Ri Pan

Authors
  1. Changjun Wu
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  2. Qiaohua Wang
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  3. Jinwei Fan
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  4. Ri Pan
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Correspondence to Changjun Wu or Qiaohua Wang.

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Wu, C., Wang, Q., Fan, J. et al. A novel prediction method of machining accuracy for machine tools based on tolerance. Int J Adv Manuf Technol 110, 629–653 (2020). https://doi.org/10.1007/s00170-020-05762-4

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