Russian Federation
Russian Federation
Russian Federation
This article analyzes 3D printing methods in various fields of manufacturing. Modern AI-driven optimization methods for 3D printing parameters were studied, aimed at solving key challenges in the field: material inhomogeneity, layer deformation caused by thermal stress, and others. Promising directions were also explored, including the adaptation of AI models for multi-material systems and optimization acceleration through quantum computing.
additive manufacturing, 3D printing, machine learning, AI, neural networks
1. Ronneberger O. U-Net: Svertochnye seti dlya biomedicinskoy segmentacii izobrazheniy // Medicinskaya komp'yuternaya tomografiya i komp'yuternoe vmeshatel'stvo (MICCAI). 2015. URL: https://lmb.informatik.uni-freiburg.de/people/ronneber/u-net/ (data obrascheniya: 13.02.2025).
2. Fyulop A., Horvat A. Skvoznoe obuchenie glubokih neyronnyh setey v chastotnoy oblasti // Mathematics. 2022. T. 10. S. 2132. DOI:https://doi.org/10.3390/math10132132. DOI: https://doi.org/10.3390/math10122132; EDN: https://elibrary.ru/CKMLVV
3. Lu H., Huan Ya., Chzhan Ch. i dr. Dostizheniya v glubokom obuchenii dlya mikroskopii sverhvysokogo razresheniya (priglashennyy doklad) // Laser & Optoelectronics Progress. 2024. T. 61, № 16. St. 1611002. DOI:https://doi.org/10.3788/LOP6124161002.
4. Mashinnoe obuchenie dlya fizicheskih nauk: vorkshop na NeurIPS 2022 [ML for Physical Sciences Workshop at NeurIPS 2022]. URL: https://ml4physicalsciences.github.io/2022/files/NeurIPS_ML4PS_2022_92.pdf (data obrascheniya: 13.02.2025).
5. Rudskoy A.I., Volkov K.N., Sokolov Yu.A., Kondrat'ev S.Yu. Cifrovye proizvodstvennye sistemy: tehnologii, modelirovanie, optimizaciya. SPb.: SPbPU, 2020. 828 s. DOI: https://doi.org/10.18720/SPBPU/2/id20-95; EDN: https://elibrary.ru/BEOOLC
6. Markidis S. Staroe i novoe: Mogut li fizicheski informirovannye metody glubokogo obucheniya zamenit' tradicionnye lineynye reshateli? // Mathematics. 2022. T. 10. S. 603. DOI:https://doi.org/10.3390/math9100603. DOI: https://doi.org/10.3390/math10040603; EDN: https://elibrary.ru/IPKZTB
7. NVIDIA Solutions. URL: https://www.macnica.co.jp/en/business/semiconductor/manufacturers/nvidia/products/134046/ (data obrascheniya: 13.02.2025).
8. Sizaya A. Modelirovanie teploperedachi i napryazheniy pri lazernoy svarke s ispol'zovaniem paketa FENICS // Angliyskiy yazyk v sfere professional'nyh kommunikaciy: Sb. dokl. VII Vseros. molodezhnoy nauch. konf. Kazan', 2021. S. 249-251.
9. Appercase News. URL: https://www.appercase.ru/news/27220/ (data obrascheniya: 13.02.2025).
10. Assured Systems. URL: https://www.assured-systems.com/nvidia-jetson-agx-xavier-for-new-era-of-ai-in-robotics/ (data obrascheniya: 13.02.2025).
11. Sigma Labs News. URL: https://3dprintingindustry.com/news/sigma-labs-printrite3d-platform-made-compatible-with-polymer-3d-printing-201503/ (data obrascheniya: 13.02.2025).
12. Nikolenko, S. D. Modelirovanie vozniknoveniya vnutrennih napryazheniy v slozhnoy strukture materiala / S. D. Nikolenko, S. P. Kozodaev, S. A. Sazonova // Modelirovanie sistem i processov. – 2024. – T. 17, № 2. – S. 50-61. – DOIhttps://doi.org/10.12737/2219-0767-2024-17-2-50-61. EDN: https://elibrary.ru/DIXFHX
13. Sazonova, S. A. Modelirovanie processa diagnostiki utechek na osnove dvuhal'ternativnoy gipotezy s uchetom pomeh ot stohastichnosti potrebleniya v gidravlicheskoy sisteme / S. A. Sazonova, I. V. Scherbakova, G. I. Smetankina // Modelirovanie sistem i processov. – 2024. – T. 17, № 1. – S. 111-120. DOI: https://doi.org/10.12737/2219-0767-2024-17-1-111-120; EDN: https://elibrary.ru/CSKRIZ
