Systematic development and evaluation of composite methods for recognition of three-dimensional subtractive features

Abstract

This paper describes a systematic procedure for developing composite feature detection systems from six methods for detecting three-dimensional depression features. The six methods, proposed by the authors in earlier papers, correspond to all the possible ways of grouping faces together from the simplest to the most complex grouping. All the possible ways of combining the six feature detection methods are considered and arranged in a tree structure. The possible composites are reduced to 20, using a tree pruning technique based on the criteria that the features detected should be the same (i.e. consistent), irrespective of the ordering of the faces in the B-rep model and that all faces of the component should be detected (i.e. complete coverage). A test bed for these 20 composites has been developed, implemented, and tested using carefully selected components from the public domain. The performance of these 20 composites is evaluated on the basis of suitability of the features as input to a machining application with minimal or no additional geometric reasoning, thus enabling the most promising composites to be identified.

Voronoi-diagram-based linking of contour-parallel tool paths for two-and-a-half-dimensional closed-pocket machining

“…(a) reads the input file which is a geometrical description of the feature, the feature having been recognized by the feature recognition module [17]; (b) identifies the base face and then constructs the Voronoi diagram for it [6]; (c) generates the contour-parallel offsets for a given tool diameter and radial width of cut [5].…”

An oriented feature extraction and recognition approach for concave-convex mixed interacting features in cast-then-machined parts

“…Existing feature recognition methods developed by numerous researchers are mainly divided into four categories, namely, the volumetric decomposition, 20–23 graph-based, 24–38 hint-based, 39–43 and artificial neural network (ANN) approaches. 44–46 By considering the massiveness and diversity of feature and variety of feature interacting ways in cabin structures, volumetric decomposition method is not applicable for its huge amount of volume segmentation operations and time costing.…”