Recognizing features from orthographic images using neural networks: a framework for CADCAM and AVI integration

Abstract

In order to satisfy the current market demands for shorter lead-time and high quality products manufacturing enterprises have to integrate their design, production and quality assurance functions. In a computerized environment these functions are manifested in CAD, CAM and AVI respectively. In the recent past, much success has been achieved in integrating CAD and CAM. It is widely accepted that feature recognition systems, both 2D and 3D, have been one of the main contributors to CADCAM integration. However the existing feature recognition systems could not link CADCAM and AVI and hence could not close the manufacturing loop that ensures the production of designer intended features. This is mainly because the input formats of existing feature recognition systems and AVI systems have been different in format and structure. The system described in this paper attempts to redress this deficit at least partially by using a monochrome bitmap as a generic input, to which both CAD models and vision images could be converted. Hence the input to the system takes a form of third angle orthographic views, however, without hidden lines in order to facilitate dealing with vision images. This, in turn, augments difficulty faced in recognizing features. To overcome this difficulty, the help of evolutionary computing and artificial intelligence is sought in this system. This paper outlines the basis of a 2D feature recognition system that uses artificial neural networks along with a chain code method for eliciting feature information from monochrome bitmap of either vision images or CAD inputs hence providing a generic framework to integrate CADCAM and AVI. With further improvement to deal with geometry of the features, annotations and symbols this system could also help to salvage the massive store of engineering knowledge that exists in 2D form.

Keywords:

• CADCAM
• AVI
• Integration
• Feature recognition
• Orthographic images
• Neural networks

Acknowledgements

Both the authors would like to thank Professor Alan Ball of the University of Birmingham for facilitating the funding of the first author's PhD research on which this paper is based. The second author would like to thank Dr Gamila Shoib of the University of Bath, Mr Mohammad Morshed of the University of Birmingham, and Farzana Sheik and Fatima Sheik for their assistance in preparing this paper.