Abstract
The article presents a method for generating synthetic data for training a neural network (hereinafter referred to as a neural network) to recognize existing objects. This method is designed to simplify the process of compiling the initial data set and modifying it for further application in the computer vision. An aircraft engine gearbox printed using additive technologies is used as a sample object for recognition. Three-dimensional models are loaded into Houdini three-dimensional editor, where a screenshot collection of the part on different backgrounds is saved using a sub-programme (hereinafter referred to as script) in Python. The received data set is applied to train three neural networks on the Roboflow website, and the results obtained are analysed for the possibility of using this method further. The article shows in detail the process of creating screenshots and the result of recognizing a printed part using three neural networks
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