An Application of Neural Networks to Group Technology
Adaptive resonance theory (ART) neural networks are being developed for application to the industrial engineering problem of group technology--the reuse of engineering designs. Two- and three-dimensional representations of engineering designs are input to ART-1 neural networks to produce groups or families of similar parts. These representations, in their basic form, amount to bit maps of the part, and can become very large when the part is represented in high resolution. This paper describes an enhancement to an algorithmic form of ART-1 that allows it to operate directly on compressed input representations and to generate compressed memory templates. The performance of this compressed algorithm is compared to that of the regular algorithm on real engineering designs and a significant savings in memory storage as well as a speed up in execution is observed. In additions, a "neural database'' system under development is described. This system demonstrates the feasibility of training an ART-1 network to first cluster designs into families, and then to recall the family when presented a similar design. This application is of large practical value to industry, making it possible to avoid duplication of design efforts.
T. P. Caudell et al., "An Application of Neural Networks to Group Technology," Proceedings of SPIE 1469, Applications of Artificial Neural Networks II, vol. 1469, pp. 612-621, SPIE--The International Society for Optical Engineering, Jan 1991.
The definitive version is available at https://doi.org/10.1117/12.44994
2nd International Conference on Applications of Artificial Neural Networks (1992: Apr. 2-5, Orlando, FL)
Electrical and Computer Engineering
International Standard Book Number (ISBN)
Article - Conference proceedings
© 1991 SPIE--The International Society for Optical Engineering, All rights reserved.