Technique of increasing the accuracy of GTE parts manufactured by selective laser melting

Степаненко, И. С.; Pechenin, V. A.; Ruzanov, N. V.; Khaimovich, Alexander

doi:10.1088/1742-6596/1096/1/012143

Cited by 4 publications

(1 citation statement)

References 9 publications

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“…At the next stage, the algorithm for searching and refining facets of one face is used. We briefly describe the steps of the algorithm for searching for facets belonging to a specific face [19]: 1) input parameters are set: V g×3 , F m×3 , N m×3 , p  (coordinate vector of a point on the surface whose facets are to be selected), t  ( normal at point p ), and α (angle tolerance to find matching facets).…”

Section: Recreation Of the Complete Model Additional Refinement Of Tmentioning

confidence: 99%

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

Bolotov

Pechenin

Ruzanov

et al. 2019

Proceedings of the v International Conference Information Technology and Nanotechnology 2019

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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.

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Section: Recreation Of the Complete Model Additional Refinement Of Tmentioning

confidence: 99%

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

Bolotov

Pechenin

Ruzanov

et al. 2019

Proceedings of the v International Conference Information Technology and Nanotechnology 2019

View full text Add to dashboard Cite

show abstract

Neural network model of machine parts classification by optical scanning results

Pechenin

Bolotov

Pechenina

2020

J. Phys.: Conf. Ser.

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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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