Surface recognition of machine parts based on the results of optical scanning

To predict the quality parameters of products (in particular, the assembly parameters) mathematical models were implemented in the form of computer models. To ensure the adequacy of calculations, it is necessary to have information about the actual geometry of the parts, which can be obtained using noncontact measurements of parts of the assembly. As a result of measuring parts and components using optical or laser scanner, a large dimension array of measured points is formed. After standard processing (e.g. noise removal, combining the scans, smoothing, creating triangulation mesh), the recognition of individual surfaces of parts becomes necessary. This paper presents a neural network model that allows the recognition of elements based on an array of measured points obtained by scanning.