3D chatter stability of high-speed micromilling by considering nonlinear cutting coefficients, and process damping. (September 2020)

• Record Type: Journal Article
• Authors: Chen, Qidi; Li, Wei; Ren, Yinghui; Zhou, Zhixiong
• Is Part Of: Journal of manufacturing processes. Volume 57(2020)
• Page Start: 552
• Page End: 565
• Publication Date: 2020-09
• Subjects: Micromilling -- Chatter stability -- Spindle speed -- Feed rate -- Tool tip dynamics; Production management -- Data processing -- Periodicals; Manufacturing processes -- Periodicals; Procestechnologie; Productietechniek; Production -- Gestion -- Informatique -- Périodiques; Fabrication -- Périodiques; Manufacturing processes; Production management -- Data processing; Periodicals; 670.5
• DOI: 10.1016/j.jmapro.2020.07.016
• Languages: English
• ISSNs: 1526-6125

Abstract

Abstract: High-speed micromilling can be used to fabricate 3D complex miniature components. The regenerative chatter, which is closely related to machining geometric accuracy, surface quality and tool wear, is a very important topic in micromachining research. In order to predict the high-speed micromilling stability more comprehensively, a micromilling force model was developed, which took into account the nonlinear change of cutting coefficients caused by feed rate, and the process damping effect in the shearing-dominant regimes. The relationship between micromilling force coefficients and feed rate were researched by using the least square and regression analysis method which are suitable to fit the relationship between related variables through experimental data, and ignore the complex internal relationships. The dynamic parameters of tool-machine system were obtained by performing FEM simulation on the entire spindle-holder-tool system. The continuous change of feed rate was first considered into micromilling 3D stability model by analyzing the influence of feed rate on milling force coefficients. Finally, micromilling tests were performed on AL7075 to verify the accuracy of the stability prediction model.