prediction of surface location error in milling considering the effects of uncertain factors

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ID: 220319
2017
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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.
Reference Key
huang2017mechanicalprediction Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors ;X. Huang;F. Jia;Y. Zhang;J. Lian
Journal advances in medical education and practice
Year 2017
DOI
10.5194/ms-8-385-2017
URL
https://www.mech-sci.net/8/385/2017/ms-8-385-2017.pdf
Keywords
materials of engineering and construction mechanics of materials

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