Journal cover Journal topic
Mechanical Sciences An open-access journal for theoretical and applied mechanics

Journal metrics

  • IF value: 1.211 IF 1.211
  • IF 5-year<br/> value: 1.705 IF 5-year
  • SNIP value: indexed SNIP
  • SJR value: indexed SJR
  • IPP value: indexed IPP
  • h5-index value: 15 h5-index 15
Supported by
Logo Library of Delft University of Technology Logo NWO
Affiliated to
Logo iftomm
Mech. Sci., 8, 385-392, 2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 4.0 License.
Research article
20 Dec 2017
Prediction of surface location error in milling considering the effects of uncertain factors
Xianzhen Huang1, Fangjun Jia1, Yimin Zhang1, and Jinhua Lian2 1School of Mechanical Engineering and Automation, Northeastern University, Shenyang, 110819, China
2Technical Center, Taiyuan Heavy Industry Co., Ltd., Taiyuan, 030024, Shanxi, China
Abstract. Machining accuracy of a milled surface is influenced by process dynamics. Surface location error (SLE) in milling determines final dimensional accuracy of the finished surface. Therefore, it is critical to predict, control, and minimize SLE. In traditional methods, the effects of uncertain factors are usually ignored during prediction of SLE, and this would tend to generate estimation errors. In order to solve this problem, this paper presents methods for probabilistic analysis of SLE in milling. A dynamic model for milling process is built to determine relationship between SLE and cutting parameters using full-discretization method (FDM). Monte-Carlo simulation (MCS) method and artificial neural network (ANN) based MCS method are proposed for predicting reliability of the milling process. Finally, a numerical example is used to evaluate the accuracy and efficiency of the proposed method.

Citation: Huang, X., Jia, F., Zhang, Y., and Lian, J.: Prediction of surface location error in milling considering the effects of uncertain factors, Mech. Sci., 8, 385-392,, 2017.
Publications Copernicus