Burr removal from measurement data of honeycomb core surface based on dimensionality reduction and regression analysis

The quantitative evaluation of the shape accuracy of the machined honeycomb cores has always been difficult, due to its typical thin-wall and low-rigidity characteristics. Laser triangulation is adopted in this paper to measure the surface shape of honeycomb cores due to its advantages of high-accuracy and high-speed, but the original measurement is not accurate enough as a result of the inclusive massive burr data. This paper presents an approach to remove burr data of each extracted cell wall based on dimensionality reduction and regression analysis. First, according to their distribution characteristics, burr data are divided into two types: burr I data and burr II data. Second, vertical and horizontal dimensionality reduction, respectively used for removing burr I data and burr II data, are applied to the measured data to reduce the dimension from three to two. Finally, in the 2D space after dimensionality reduction, the distribution line of the cell wall is forecasted with regression analysis, and burrs are removed according to its distance to the distribution line. Experimental results show that the proposed method has an outstanding performance in removing burr data on various shapes of surfaces.