Optimization of Milling Parameters for Titanium Alloys Based on Support Vector Machine (Machine Learning) and Ant Colony Optimization Algorithm

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In this paper, the titanium alloy milling process is analysized by finite element method, and the processing quality of this materials will be affected by the milling force. A milling force prediction model was established based on support vector machine (SVM) and ant colony optimization (ACO) for titanium alloy milling process cutting parameters. The main feature of this model design methodology is to hybridize the solution construction mechanism of ant colony optimization (ACO) with support vector machine (SVM) regression based on HYPERLINK "https://cn.mathworks.com/discovery/supervised-learning.html" supervised learning algorithm. The results show that this methodology is very efficient, and thus can be used in the machining process parameters optimum and other material processing fields.

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Periodical:

Edited by:

Huiping Tang, Ma Qian, Yong Liu, Peng Cao and Gang Chen

Pages:

262-267

Citation:

X. X. Zhang et al., "Optimization of Milling Parameters for Titanium Alloys Based on Support Vector Machine (Machine Learning) and Ant Colony Optimization Algorithm", Key Engineering Materials, Vol. 770, pp. 262-267, 2018

Online since:

May 2018

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$38.00

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