Recognition of maximal features by volume decomposition

doi:10.1016/S0010-4485(01)00080-X

Computer-Aided Design

Volume 34, Issue 3, March 2002, Pages 195-207

https://doi.org/10.1016/S0010-4485(01)00080-X Get rights and content

Abstract

Multiple feature interpretations and lack of scalability have been issues in the latest feature recognition research. A feature recognition method has been developed that handles these issues well. The method recognizes a set of machining features called maximal features in a designed part by decomposing the delta volume, which is the difference between the stock and the part. Maximal features are intermediate features. By performing process planning with maximal features, the optimal or satisfactory feature interpretation will be found. Scalability has been achieved by adopting a divide-and-conquer approach in the process of decomposing the delta volume.

Section snippets

Background

Despite the long history of research in automated process planning, at present there does not exist any commercial automated process planning system. A machinist still plans the setup sequence, fixture, and machining sequence including cutter selection, and then generates cutter paths using an interactive CAM program. One lacking component in realizing automated process planning has been a suitable model of the designed part for automated process planning. Feature-based model has been viewed as

Previous work

In the last decade or so, several methods that can recognize intersecting features in moderately complex real-world parts have been developed. All of these methods recognize features as volumes. One group of such methods [1], [2], [3], called hint-based or trace-based feature recognition, took advantage of the fact that an end-milling cutter can create only milling features each of which has a planar floor face and some planar or cylindrical wall faces perpendicular to the floor face. The

Maximal feature

To avoid confusion, our definitions of machining feature and feature interpretation are presented first. A machining featureor simply feature of a part is a volume that satisfies the following conditions.

1.
It is contained in the delta volume.
2.
It can be removed from the workpiece by one machining operation with a 3-axis machining center. One machining operation means a movement of one cutter in one setup without retracting from the workpiece.
3.
Its removal creates a portion of the part surface without

Process of recognizing maximal features

The approach to recognizing maximal features is that we first decompose the delta volume into large and simple volumes called maximal volumes, which are explained in the next section, and then transform them into maximal features. Fig. 5 shows the process of maximal feature recognition. The process consists of three steps: maximal volume decomposition, selection, and conversion.

Step 1

Maximal volume decomposition. The delta volume is decomposed into its maximal volumes. For example, the delta

Conclusions

It has been proposed to recognize maximal features from which the optimal or satisfactory feature interpretation can be generated in process planning. Then a feature recognition method that recognizes maximal features has been presented. Scalability of the method was achieved by adopting a divide-and-conquer approach in the decomposition process. The method successfully recognized maximal features of some complex real-world parts within reasonable time.

One future work is to verify that the

Yoonhwan Woo is a software engineer at Spatial Corporation in Boulder, Colorado. He received his PhD degree in Mechanical Engineering from Colorado State University, the Master degree in Mechanical and Aerospace Engineering from Illinois Institute of Technology, and the BSc degree in Precision Mechanical Engineering from Hanyang University, Korea. His research interests include geometric and solid modeling, CAD/CAM, and computer-aided process planning.

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Hiroshi Sakurai is an Associate Professor of Mechanical Engineering at Colorado State University. He received his PhD in Mechanical Engineering from Massachusetts Institute of Technology in 1990, and his MSc in Mechanical Engineering also from the Massachusetts Institute of Technology in 1982. His research interests are in the area of computer-aided design and manufacturing, geometric modeling, and computer graphics.

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