Reverse engineering of geometric models is the process of creating a computer aided model from an existing physical part so that subsequent manufacturing processes may be implemented. Applications of reverse engineering can range from the production of molds and dies from wood or clay models to the creation of replacement parts from worn existing machinery. In reverse engineering, both contact and non-contact measurement probes are used to gather measured surface points. However, due to the nature of these instruments, both the direction of the probe during measurement and the conversion of the gathered data to the appropriate computer aided models are currently very difficult.

This thesis addresses some of these problems. A stereo vision system employing neural network based image segmentation is implemented to automatically generate probe paths for either a touch trigger probe or an optical laser scanner. A fuzzy logic based iterative geometry fitting algorithm is used to fit geometric primitives to measured surface data. As modem computer aided drafting programs utilise parametric modelling methods and topology information, regarding the association of neighbouring surface patches is determined from the fitted geometric entities. Finally, utilising the extracted geometric and topology information, specific surface features, such as comers, slots and steps are detected using a feed-forward neural network.

The computational tools in this thesis provide methods that reduce the time and effort required to geometrically reverse engineer an existing physical object.