Prediction model of machining errors based on precision and process parameters of machine tools

被引：0
|
作者：
Xiong Q. [1 ,2 ]
Wang J. [1 ]
Zhou Q. [1 ]
机构：
[1] School of Aeronautics and Astronautics, Sichuan University, Chengdu
[2] Chengdu Aircraft Industry (Group) Co., Ltd., Chengdu
来源：
Hangkong Xuebao/Acta Aeronautica et Astronautica Sinica | 2018年 / 39卷 / 08期
关键词：
Aircraft structural part; BP neural network; CNC milling machine; Prediction of machining error; Process parameter;
D O I：
10.7527/S1000-6893.2018.21713
中图分类号：
学科分类号：
摘要：
To overcome the deficiency of machining accuracy evaluation system of five-axis NC milling machine in processing aircraft structural parts, an evaluation system is constructed using machine tool precision detection data, characteristics structural parts and their machining parameters. Based on the BP neural network, a prediction model for machining errors of five-axis NC milling machine is built up. The influence of each input index on the evaluation result is calculated through weight distribution of the trained network, and effectiveness of the model is verified by an example. It is shown that the results obtained by the BP neural network model are in good agreements with those by the coordinate measuring machine, demonstrating the effectiveness of those selected evaluation indexes. The prediction model can effectively evaluate the processing accuracy of the five-axis NC milling machine by combining the machine tool precision detection data, characteristics of the parts and process parameters. © 2018, Press of Chinese Journal of Aeronautics. All right reserved.
引用
收藏
相关论文
共 20 条
  • [1] Shuai C.L., Digital design and manufacturing technology for aircraft structural parts, Aeronautical Manufacturing Technology, 1, pp. 48-52, (2016)
  • [2] Li J., Xie F.G., Liu X.J., Et al., Analysis on the research status of volumetric positioning accuracy improvement methods for five-axis NC machine tools, Journal of Mechanical Engineering, 53, 7, pp. 113-128, (2017)
  • [3] Lu B.H., Ge Y.J., Wang Q.Y., Et al., Study on the precision prediction for virtual NC turning, Journal of Mechanical Engineering, 38, 2, pp. 82-85, (2002)
  • [4] Dang J.W., Zhang W.H., Wan M., Et al., A novel model for predicting machining error in peripheral milling process, Journal of Mechanical Engineering, 47, 17, pp. 150-155, (2011)
  • [5] Li F.C., Wang H.T., Li T.M., Research on thermal error modeling and prediction of heavy CNC machine tools, Journal of Mechanical Engineering, 52, 11, pp. 154-160, (2016)
  • [6] Song G., Li J.F., Sun J., Analysis on prediction of surface error based on precision milling cutting force model, Journal of Mechanical Engineering, 49, 21, pp. 168-175, (2013)
  • [7] Wang F.L., Dong Z.G., Wu Z.H., Et al., Optimization of maize planting density and fertilizer application rate based on BP neural network, Transactions of the Chinese Society of Agricultural Engineering, 33, 6, pp. 92-99, (2017)
  • [8] Xu L.M., Wang Q., Chen J.P., Et al., Forecast for average velocity of debris flow based on BP neural network, Journal of Jilin University (Earth Science Edition), 43, 1, pp. 186-191, (2013)
  • [9] Deng Q., Ma Y.F., Liu Y., Et al., Prediction of retweet counts by a back propagation neural network, Journal of Tsinghua University (Science and Technology), 55, 12, pp. 1342-1347, (2015)
  • [10] Guo R.H., Su T.T., Ma X.W., License plate recognition system using a BP neural network and template matching, Journal of Tsinghua University (Science and Technology), 53, 9, pp. 1221-1226, (2013)
12