Micromechanical manufacturing based on microequipment creates new
possibi- ties in goods production. If microequipment sizes are
comparable to the sizes of the microdevices to be produced, it is
possible to decrease the cost of production drastically. The main
components of the production cost - material, energy, space consumption,
equipment, and maintenance - decrease with the scaling down of equipment
sizes. To obtain really inexpensive production, labor costs must be
reduced to almost zero. For this purpose, fully automated microfactories
will be developed. To create fully automated microfactories, we propose
using arti?cial neural networks having different structures. The
simplest perceptron-like neural network can be used at the lowest levels
of microfactory control systems. Adaptive Critic Design, based on neural
network models of the microfactory objects, can be used for
manufacturing process optimization, while associative-projective neural
n- works and networks like ART could be used for the highest levels of
control systems. We have examined the performance of different neural
networks in traditional image recognition tasks and in problems that
appear in micromechanical manufacturing. We and our colleagues also have
developed an approach to mic- equipment creation in the form of
sequential generations. Each subsequent gene- tion must be of a smaller
size than the previous ones and must be made by previous generations.
Prototypes of ?rst-generation microequipment have been developed and
assessed.
