Graph-based heuristics for recognition of machined features from a 3D solid model
Computer-Aided Design
Convex hull-based feature-recognition method for 2.5D components
Computer-Aided Design
Automated feature recognition from 2D CAD models
Automated feature recognition from 2D CAD models
Fixturing design analysis for successive feature-based machining
Computers in Industry
STEP-based feature extraction from STEP geometry for Agile manufacturing
Computers in Industry
Novel ANN-based feature recognition incorporating design by features
Computers in Industry
An Internet-Based System for Setup Planning in Machining Operations
ICECCS '05 Proceedings of the 10th IEEE International Conference on Engineering of Complex Computer Systems
An Internet-Enabled Setup Planning System
ICITA '05 Proceedings of the Third International Conference on Information Technology and Applications (ICITA'05) Volume 2 - Volume 02
A new methodology for extracting manufacturing features from CAD system
Computers and Industrial Engineering
Sequencing of interacting prismatic machining features for process planning
Computers in Industry
Online high resolution machining process optimal control
CONTROL'09 Proceedings of the 5th WSEAS international conference on Dynamical systems and control
Review: A review and analysis of current computer-aided fixture design approaches
Robotics and Computer-Integrated Manufacturing
Computers and Industrial Engineering
Feature-based generation of machining process plans for optimised parts manufacture
Journal of Intelligent Manufacturing
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For seamless automation, computer-aided design and manufacturing activities have to be linked by computer-aided process planning (CAPP). An important subtask in CAPP is setup planning, in which a setup plan must be generated ideally from a given 3-D model of the component. In this paper, a hybrid approach that effectively uses volume subtraction and face adjacency graph is proposed to recognize manufacturing features from 3-D model data in STEP AP-203 format. The proposed feature recognition is generic in nature and is capable of recognizing intersecting features also with relative ease. The manufacturing features are clustered based on preferential base for machining and a setup sequence is obtained by alternative rating and ranking. Finally, locating and clamping for each setup are determined considering intermediate shapes of the workpiece. This setup planning method reduces the number of alternatives for evaluation and thereby the computational effort.