030070008 Bahadır Coşkun (5th Week)

Cell decomposition (10 March 2011 07:27)
Cell decomposition techniques create a finite number of cells out of the continuous free space. The motion-planning problem is then reduced to that of finding a connected sequence of empty cells from the start configuration to the goal configuration. The basic technique of cell composition maybe stated in four simple steps.

Divide the free space into a finite number of connected regions called “cells”.

Construct a cell adjacency graph. The adjacency graph vertices are the cells themselves, and the edges connect cells that about each other.

Determine which cells the start and end configurations lie in and find a path on the adjacency graph between these two cells.

For each cell in the sequence of cells determined in the graph search, find a path from a point on the boundary of the cell to a point on the boundary of the previous cell.

(Balakirsky S.B., A framework for planning with incrementally created graphs in attributed program spaces, p.44)

Entity Growing (10 March 2011 07:54)
Entity growing methods have been developed to generate volumetric features from surface features that have already been recognized. There are a number of methods developed. The edges of a face, other than the face that belongs to the surface feature, are extended to generate volume(s) through creating new edges and vertices. Feature volumes may also be created by adding half spaces corresponding to feature faces.

(Xun Xu, Integrating Advanced Computer-Aided Design, Manufacturing, and Numerical Control: Principles and Implementations, p.99)

Backward Growing (10 March 2011 08:00)
The backward growing approach reverses the machining/removal process by growing properly machined volume/feature back to other machined face(s) (Figure 5.6). Basic cavity features were generated, and compound and protrusion features were decomposed into basic ones by adding surrounding materials. During the growing procedure, manufacturing parameters, such as types of basic elementary machined shapes, tool approach directions, precedence between recognized features, refined features, and intermediate work piece specifications/shapes may also be determined.

(Xun Xu, Integrating Advanced Computer-Aided Design, Manufacturing, and Numerical Control: Principles and Implementations, p.98-99)

Feature Detection (10 March 2011 08:10)
Algorithms used for feature detection normally vary with the different feature representation schemes adopted in the feature libraries. Nevertheless, specific tasks in feature detection may include the following (although it is not necessary to have all of them included in a system),
• re-constituting (re-constructing) the geometric model topologically and/or geometrically;
• searching the database to match topologic/geometric patterns with those in the feature library;
• extracting detected feature(s) from the database;
• completing the feature geometric model;
• analyzing and/or re-constructing features.
It is noted that most feature detection techniques are based on the depression-oriented approach, and they largely depend upon the different feature representation schemes employed. There is a number of feature detection techniques developed. They are; (a) graph-based method; (b) syntax-based method; (c) rule-based method and (d) techniques for recognizing feature from CSG models.
(Xun Xu, Integrating Advanced Computer-Aided Design, Manufacturing, and Numerical Control:Principles and Implementations, p.92)