Abstract
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.
Highlights
Cutting vibrations can adversely affect machining accuracy of a workpiece during the milling process
Compared with the conventional approaches, randomness of parameters is taken into account in the proposed approach, which makes the prediction of surface location error (SLE) in milling more correspond to engineering practice
A significant improvement can be made in SLE prediction in milling, by taking parameter uncertainty into account
Summary
Cutting vibrations can adversely affect machining accuracy of a workpiece during the milling process. Mann et al(2005) proposed an approach for simultaneous prediction of SLE in milling using Temporal Finite Element Analysis (TFEA). This method was generalized for helical end mills (Mann et al, 2008). As a result, underlying principle of milling dynamics is fairly well established All these models assume that parameters of the milling process such as depth of cut, feed per tooth and cutting force are deterministic. A practical method is proposed to predict SLE in milling considering the effects of uncertain factors. X. Huang et al.: Prediction of surface location error in milling considering the effects of uncertain factors. A numerical application is provided to verify the accuracy and efficiency of the proposed approach
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
