Image processing-based contour parallel tool path optimization for arbitrary pocket shape

Contour-parallel tool path has been a popular means in milling 2D pocket regions. Traditional contour-parallel path suffers from unsmooth transition and uncut problem around corners, which requires dedicated local geometric treatment. In addition, these two issues contradict with each other; solving one may jeopardize the other. Motivated by these problems, we present a digital image approach to deal with the contour parallel tool path optimization. By initially converting the pocket boundary into a binary image, a greyscale image is constructed via an inward-marching algorithm, such that the greyscale value of every pixel inside the pocket indicates the shortest Euclidean distance (SED) towards the boundary. The contour-parallel tool path can be easily derived from this SED image as the iso-contours. Distinguished from the geometry-based treatments towards unsmooth tool path and uncut regions, the proposed framework takes advantage of various image processing techniques (such as Gaussian blur and unsharp masking) to the SED image. By adaptively optimizing the parameters for image blurring and sharpness, the corresponding iso-SED tool path eliminates the uncut residues while still satisfies a specified level of smoothness. A quantitative analysis together with an image optimization scheme is also presented in this work.