Method for detecting defects in direct metal deposition using a neural network

P. Pivkin; N. Khodanovich; S. Grigoriev; P. Peretyagin

doi:10.1117/12.3022863

20 June 2024 Method for detecting defects in direct metal deposition using a neural network

P. Pivkin, N. Khodanovich, S. Grigoriev, P. Peretyagin

Proceedings Volume 13005, Laser + Photonics for Advanced Manufacturing; 1300511 (2024) https://doi.org/10.1117/12.3022863
Event: SPIE Photonics Europe, 2024, Strasbourg, France

Conference Poster

Abstract

Research of literature dedicated to neural networks and artificial intelligence was made. The topicality of the research in this area was proven. Neural network models most using in computer vision were chosen. The possibility of using a method to detect defects during the process of direct metal deposition based on neural network application was proven. Three neural network models based on U-Net, ResUNet and VGG-16 architectures were trained. The best of three models was chosen. Goals for future researches were set.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

P. Pivkin, N. Khodanovich, S. Grigoriev, and P. Peretyagin "Method for detecting defects in direct metal deposition using a neural network", Proc. SPIE 13005, Laser + Photonics for Advanced Manufacturing, 1300511 (20 June 2024); https://doi.org/10.1117/12.3022863

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