A deployed engineering design retrieval system using neural networks

Abstract

We describe a neural information retrieval system (NIRS), now in production within the Boeing Company, which has been developed for the identification and retrieval of engineering designs. Two-dimensional and three-dimensional representations of engineering designs are input to adaptive resonance theory (ART-1) neural networks to produce clusters of similar parts. The trained networks are then used to recall an appropriate cluster when queried with a new part design. This application is of great practical value to industry because it aids in the identification, retrieval, and reuse of engineering designs, potentially saving large amounts of nonrecurring costs. In this paper, we review the application, the neural architectures and algorithms, and then give the current status and the lessons learned in developing a neural network system for production use in industry.