Currently, most of CAD/CAM systems are feature-based. Features are important to maintain geometric and non-geometric relations across product models. Often, during data translation some useful geometric and non-geometric information such as features and their associated semantics are stripped off. In this paper, we introduce a new method of feature-based modeling in product data exchange using ...

This paper discusses an algorithm incorporating a knowledge-based vision system into an industrial robot system for handling parts intelligently. A continuous fuzzy controller was employed to extract boundary information in a computationally efficient way. The developed algorithm for on-line part recognition using fuzzy logic is shown to be an effective solution to extract the geometric feature...

Determining the precedence of machining features is a critical issue in feature-based process planning. It becomes more complex when geometric interaction occurs between machining features. STEP-NC, the extension of STEP (ISO 10303) standard developed for CNC controllers, is a feature-based data model. It represents all the geometric and topological product data minus feature interactions. In t...

Object recognition is the process of identifying and locating known objects in complex images. It includes extracting relevant features, grouping these features together, selecting an appropriate object model, and determining the pose (position and orientation) of the object in the scene. In earlier work, we have shown that fuzzy methods are appropriate for representing geometric relationships ...

Approximate geometric models, e.g. as created by reverse engineering, describe the approximate shape of an object, but do not record the underlying design intent. Automatically inferring geometric aspects of the design intent, represented by feature trees and geometric constraints, enhances the utility of such models for downstream tasks. One approach to design intent detection in such models i...

Methods for generating a closed geometric model from a point-sampled volume data set can greatly enhance visualization of noisy volume data collected from nondestructive sensing instruments such as MRI. Traditional methods attempt to perform segmentation automatically, by identifying boundary layers using a parametric snake. Essentially, an initial model, called a ‘seed’ is placed inside the it...