摘要
A honeycomb structure is widely used in sandwich structure components in aeronautics and astronautics;however,machining is required to reveal some of its features.In honeycomb structures,deficiencies,such as burrs,edge subsiding,and cracking,can easily appear,owing to poor specific sti ness in the radial direction.Some e ective fixation methods based on a filling principle have been applied by researchers,including approaches based on wax,polyethylene glycol,iron powder,and(especially)ice.However,few studies have addressed the optimization of the cutting parameters.This study focused on optimizing the cutting parameters to obtain a better surface roughness(calculated as a roughness average or Ra)and surface morphology in the machining of an aluminum alloy honeycomb by an ice fixation method.A Taguchi method and an analysis of variance were used to analyze the e ects and contributions of spindle speed,cutting depth,and feed rate.The optimal cutting parameters were determined using the signal-to-noise ratio combined with the surface morphology.An F-value and P-value were calculated for the value of the Ra,according to a"smaller is better"model.Additionally,the optimum cutting parameters for machining the aluminum honeycomb by ice fixation were found at different levels.The results of this study showed that the optimal parameters were a feed rate of 50 mm/min,cutting depth of 1.2 mm,and spindle speed of 4000 r/min.Feed rate was the most significant factor for minimizing Ra and improving the surface morphology,followed by spindle speed.The cutting depth had little e ect on Ra and surface morphology.After optimization,the value of Ra could reach 0.218μm,and no surface morphology deterioration was observed in the verified experiment.Thus,this research proposes optimal parameters based on ice fixation for improving the surface quality.
A honeycomb structure is widely used in sandwich structure components in aeronautics and astronautics; however, machining is required to reveal some of its features. In honeycomb structures, deficiencies, such as burrs, edge subsiding, and cracking, can easily appear, owing to poor specific sti ness in the radial direction. Some effective fixation methods based on a filling principle have been applied by researchers, including approaches based on wax, polyethylene glycol, iron powder, and(especially) ice. However, few studies have addressed the optimization of the cutting parameters. This study focused on optimizing the cutting parameters to obtain a better surface roughness(calculated as a roughness average or Ra) and surface morphology in the machining of an aluminum alloy honeycomb by an ice fixation method. A Taguchi method and an analysis of variance were used to analyze the effects and contributions of spindle speed, cutting depth, and feed rate. The optimal cutting parameters were determined using the signal-to-noise ratio combined with the surface morphology. An F-value and P-value were calculated for the value of the Ra, according to a "smaller is better" model. Additionally, the optimum cutting parameters for machining the aluminum honeycomb by ice fixation were found at different levels. The results of this study showed that the optimal parameters were a feed rate of 50 mm/min, cutting depth of 1.2 mm, and spindle speed of 4000 r/min. Feed rate was the most significant factor for minimizing Ra and improving the surface morphology, followed by spindle speed. The cutting depth had little effect on Ra and surface morphology. After optimization, the value of Ra could reach 0.218 μm, and no surface morphology deterioration was observed in the verified experiment. Thus, this research proposes optimal parameters based on ice fixation for improving the surface quality.
基金
Supported by National Key Research and Development Program of China(Grant No.2019YFB2005400)
National Natural Science Foundation of China(Grant No.U1608251)
Open project of State Key Laboratory of high performance complex manufacturing(Grant No.Kfkt2016-05)
Changjiang Scholar Program of Chinese Ministry of Education(Grant No.T2017030).
