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Automatic recognition of machining features using artificial neural networks

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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.

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Authors and Affiliations

  1. Computer Aided Manufacturing Laboratory, Mechanical Engineering Department, Indian Institute of Technology Bombay, Powai, Mumbai, 400076, India

    V. B. Sunil & S. S. Pande

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  1. V. B. Sunil
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  2. S. S. Pande
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Correspondence to S. S. Pande.

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Sunil, V.B., Pande, S.S. Automatic recognition of machining features using artificial neural networks. Int J Adv Manuf Technol 41, 932–947 (2009). https://doi.org/10.1007/s00170-008-1536-z

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