It is possible to construct useful multimedia associative memories for troubleshooting, in the performance of industrial tasks, relying on the concept of self-organization and mapping of experiences. This paper demonstrates this concept for some aspects of metal casting. The theoretical issue is the question of whether it is possible to cluster and map in semantic vector spaces automatically, with little or no prior knowledge of the problem domain. The paper also reports on some early positive research results. Clustering is clearly feasible and useful, even when features are linguistic symbols rather than numerical ones.
Mapping seems to be feasible if there is, indeed, an implied relationship.
Special emphasis is placed on the use of modern graphical simulation tools, and the application and experimental verification of the techniques as applied to a vertical hydraulic forge press located at Wright-Patterson Air Force Base are described. The Application of Neural Networks in the Modeling of Plate Rolling Processes Antonio Augusto Gorni Neural networks are a relatively new artificial intelligence technique that emulate the behavior of biological neural systems in digital software or hardware. This feature makes the technique very useful in modeling processes for which mathematical modeling is difficult or impossible. The application of random inputs to the internal architecture of a trained neural network allows us to interrogate the conceptual space contained therein. Supervising this stream of potential chemical formulas with a second neural network, trained to recognize valuable potential chemical compounds, it possible to capture the emerging discoveries and, thus, create libraries of totally new commercially and technologically useful materials.