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Mechanical Sciences An open-access journal for theoretical and applied mechanics
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• IF 1.052
• IF 5-year
1.567
• CiteScore
1.92
• SNIP 1.214
• IPP 1.47
• SJR 0.367
• Scimago H
index 18
• h5-index 16

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Mech. Sci., 8, 385-392, 2017
https://doi.org/10.5194/ms-8-385-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 4.0 License.
Mech. Sci., 8, 385-392, 2017
https://doi.org/10.5194/ms-8-385-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 20 Dec 2017

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 Xianzhen Huang et al.
• 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.

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