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
  • [11] Yao Y.S., Xu K.J., Quality assessment of flight training based on BP neural network, Acta Aeronautica et Astronautica Sinica, 38, (2017)
  • [12] Fang P., Tan Y.M., Wu L., Et al., Artificial neural network applied to the quality control in alternating-current resistance spot welding, Acta Aeronautica et Astronautica Sinica, 21, 1, pp. 94-105, (2000)
  • [13] Chen Y.G., Luo X.L., Nonlinear dynamic assessment model of airline fleet equipment reliability, Acta Aeronautica et Astronautica Sinica, 34, 1, pp. 104-111, (2013)
  • [14] Ji T.J., An J.F., He S.M., Et al., Depth prediction of rain water on road surface based on artificial neural network, Transaction of Nanjing University of Aeronautics & Astronautics, 23, 2, pp. 115-119, (2006)
  • [15] Sun C.H., Zhu D., Li Z.Y., Et al., Prediction of the workpiece accuracy during the electrochemical machining based on BP neural network, Journal of South China University of Technology (Natural Science Edition), 32, 10, pp. 24-27, (2004)
  • [16] Xu X.M., Tu Y.Z., Fan S.W., Et al., Real time error compensation control scheme for virtual axis machine tools based on BP neural network, Journal of Sichuan University (Engineering Science Edition), 35, 1, pp. 81-84, (2003)
  • [17] Hornik K., Stinchcombe M., White H., Multilayer feedforward networks are universal approximators, Neural Networks, 2, 5, pp. 359-366, (1989)
  • [18] Fausett L.V., Fundamentals of Neural Networks: Architectures, Algorithms, and Applications, (1994)
  • [19] Zhang Y.W., Qi J.Y., Fang B.X., Et al., The indicator system based on BP neural network model for net-mediated public opinion on unexpected emergency, China Communications, 8, 2, pp. 42-51, (2011)
  • [20] He C.H., Huang Q.Y., Shen S.F., Et al., Forest fuel loading estimates based on a back propagation neural network, Journal of Tsinghua University (Science and Technology), 51, 2, pp. 230-233, (2011)
12