Abstract
The article considers the solution to an automation problem for assembly processes associated with determining a type of the part delivered for assembling. The parts delivered for assembling are preliminarily measured with an optical scanner. In order to solve the problem for determining the part type, convolution neural network organization was matched with the data classification. Stl-patterns for three turbine rotor parts were taken, the training and test samples were simulated, several convolution neural networks were trained and the optimal parameters ensuring 100% classification accuracy were selected.
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