A novel grooving method for eliminating the bending-induced collapse of hexagonal honeycombs has been proposed,which lies in determining the appropriate grooving parameters,including the grooving spacing,angle,and dep...A novel grooving method for eliminating the bending-induced collapse of hexagonal honeycombs has been proposed,which lies in determining the appropriate grooving parameters,including the grooving spacing,angle,and depth.To this end,a framework built upon the experiment-based,machine learning approach for grooving parameters prediction was presented.The continuously grooved honeycomb bending experiments with various radii,honeycomb types,and thicknesses were carried out,and then the deformation level of honeycombs at different grooving spacing was quantitatively evaluated.A criterion for determining the grooving spacing was proposed by setting an appropriate tolerance for the out-of-plane compression strength.It was found that as the curvature increases,the grooving spacing increases due to the deformation level of honeycombs being more severe at a smaller bending radius.Besides,the grooving spacing drops as the honeycomb thickness increases,and the cell size has a positive effect on the grooving spacing,while the relative density has a negative effect on the grooving spacing.Furthermore,the data-driven Gaussian Process(GP)was trained from the collected data to predict the grooving spacing efficiently.The grooving angle and depth were calculated using the geometrical relationship of honeycombs before and after bending.Finally,the grooving parameters design and verification of a honeycomb sandwich fairing part were conducted based on the proposed grooving method.展开更多
基金the National Natural Science Foundation of China(No.11902256)the Natural Science Basic Research Program of Shaanxi,China(No.2019JQ-479).
文摘A novel grooving method for eliminating the bending-induced collapse of hexagonal honeycombs has been proposed,which lies in determining the appropriate grooving parameters,including the grooving spacing,angle,and depth.To this end,a framework built upon the experiment-based,machine learning approach for grooving parameters prediction was presented.The continuously grooved honeycomb bending experiments with various radii,honeycomb types,and thicknesses were carried out,and then the deformation level of honeycombs at different grooving spacing was quantitatively evaluated.A criterion for determining the grooving spacing was proposed by setting an appropriate tolerance for the out-of-plane compression strength.It was found that as the curvature increases,the grooving spacing increases due to the deformation level of honeycombs being more severe at a smaller bending radius.Besides,the grooving spacing drops as the honeycomb thickness increases,and the cell size has a positive effect on the grooving spacing,while the relative density has a negative effect on the grooving spacing.Furthermore,the data-driven Gaussian Process(GP)was trained from the collected data to predict the grooving spacing efficiently.The grooving angle and depth were calculated using the geometrical relationship of honeycombs before and after bending.Finally,the grooving parameters design and verification of a honeycomb sandwich fairing part were conducted based on the proposed grooving method.
