Automatic recognition of machining features using artificial neural networks

Abstract

We report on the development of an intelligent system for recognizing prismatic part machining features from CAD models using an artificial neural network. A unique 12-node vector scheme has been proposed to represent machining feature families having variations in topology and geometry. The B-Rep CAD model in ACIS format is preprocessed to generate the feature representation vectors, which are then fed to the neural network for classification. The ANN-based feature-recognition (FR) system was trained with a large set of feature patterns and optimized for its performance. The system was able to efficiently recognize a wide range of complex machining features allowing variations in feature topology and geometry. The data of the recognized features was post-processed and linked to a feature-based CAPP system for CNC machining. The FR system provided seamless integration from CAD model to CNC programming.