Research on Prediction Technology for Tool Rotation Profile Errors in Flank Milling

Hangzhuo Yu, Shengfeng Qin, Guofu Ding, Lei Jiang*, Hongqin Liang

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

In the flank milling processes, it is difficult to accurately predict the tool rotation profile errors caused by tool wears and deformations before actual machining. A identification test method of workpiece shape-tool profile mapping was proposed to obtain the tool rotation profile errors and the error data under different working conditions were obtained through the multi-factor orthogonal tests. A prediction model for tool rotation profile error was established by the LS-SVM technology based on the error data. The genetic algorithm (GA) was used to optimize the model parameters including kernel function parameters and error warning factors, which were very important to the proposed model. A LS-SVM model was established based on GA optimization(GA-LS-SVM), which was compared with a LS-SVM model without GA optimization. The testing results show that the GA-LS-SVM prediction model is more suitable for tool rotation profile error prediction.

Translated title of the contributionResearch on Prediction Technology for Tool Rotation Profile Errors in Flank Milling
Original languageChinese (Traditional)
Pages (from-to)306-313
Number of pages8
JournalZhongguo Jixie Gongcheng/China Mechanical Engineering
Volume31
Issue number3
DOIs
Publication statusPublished - 10 Feb 2020

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