Optimization of process parameters in the high-speed milling of titanium alloy TB17 for surface integrity by the Taguchi-Grey relational analysis method

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Abstract

TB17 is an ultra-high-strength titanium alloy, a material typically difficult to machine. The surface quality and integrity of titanium alloy TB17 are poor after machining, which can seriously affect its service performance and restrict its fields of application. Therefore, the optimization of high-speed milling process parameters for the new ultra-high-strength titanium alloy TB17 was investigated based on multiple performance characteristics, including surface roughness, surface microhardness, and surface residual stress. The Taguchi method with grey relational analysis was utilized for the experiments. Additionally, analysis of variance was employed to evaluate the most influential factors for surface integrity in the high-speed milling of titanium alloy TB17. The results of the analysis using the Taguchi-Grey relational analysis method indicate that the preferred combination of high-speed milling process parameters are as follows: the cutting fluid condition of H-1 fine grinding fluid, using a milling speed of 100 m/min, a feed per tooth of 0.02 mm/z, an axial depth of cut of 1 mm, a radial depth of cut of l.5 mm, a rake angle of 18°, a clearance angle of 12°, and a helix angle of 60°. Moreover, the analysis of variance reveals that milling speed has the greatest effect on the surface integrity in the high-speed milling of the TB17 titanium alloy, and the contribution percentages for each factor are as follows: the cutting fluid condition (3.98%), milling speed (25.89%), feed per tooth (8.96%), axial depth of cut (1.29%), radial depth of cut (13.71%), rake angle (17.17%), clearance angle (6.62%), and helix angle (15.64%).

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Du, S., Chen, M., Xie, L., Zhu, Z., & Wang, X. (2016). Optimization of process parameters in the high-speed milling of titanium alloy TB17 for surface integrity by the Taguchi-Grey relational analysis method. Advances in Mechanical Engineering, 8(10), 1–12. https://doi.org/10.1177/1687814016671442

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