Reduction of material usage in 3D printable structures using topology optimization accelerated with U-Net convolutional neural network

Today’s 3D printers allow the creation of very advanced structures from various materials, starting from simple plastics up to metal alloys. Since the printing time and amount of material used to create structures are considered very important in terms of cost and energy consumption, it is better to...

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Main Authors: Rasulzade, J., Maksum, Y., Nogaibayeva, M., Rustamov, S., Akhmetov, Bakytzhan
Other Authors: School of Mechanical and Aerospace Engineering
Format: Article
Language:English
Published: 2024
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Online Access:https://hdl.handle.net/10356/173663
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-1736632024-02-24T16:47:57Z Reduction of material usage in 3D printable structures using topology optimization accelerated with U-Net convolutional neural network Rasulzade, J. Maksum, Y. Nogaibayeva, M. Rustamov, S. Akhmetov, Bakytzhan School of Mechanical and Aerospace Engineering Engineering Material reduction 3D printing Today’s 3D printers allow the creation of very advanced structures from various materials, starting from simple plastics up to metal alloys. Since the printing time and amount of material used to create structures are considered very important in terms of cost and energy consumption, it is better to optimize structures for that particular application taking into account all the conditions. In the current work, U-Net convolutional neural network-based topology optimization method (TO) that allows to reduce the material usage and eventually reduces the cost of 3D printing is introduced. The results showed that the accuracy of the method is highly reliable and can be used for designing various 3D printable structures and it applies to any type of materials since properties of materials can be included in TO. Published version This work has been supported by the research programme of Science Committee of the Ministry of Education and Science of the Republic of Kazakhstan (Grant No. AP08856141). 2024-02-21T05:08:08Z 2024-02-21T05:08:08Z 2022 Journal Article Rasulzade, J., Maksum, Y., Nogaibayeva, M., Rustamov, S. & Akhmetov, B. (2022). Reduction of material usage in 3D printable structures using topology optimization accelerated with U-Net convolutional neural network. Eurasian Chemico-Technological Journal, 24(4), 277-286. https://dx.doi.org/10.18321/ectj1471 1562-3920 https://hdl.handle.net/10356/173663 10.18321/ectj1471 2-s2.0-85145330985 4 24 277 286 en Eurasian Chemico-Technological Journal © 2022 The Author(s). Published by al-Farabi Kazakh National University. This is an open access article under the (http://creativecommons.org/licenses/by/4.0/). application/pdf
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic Engineering
Material reduction
3D printing
spellingShingle Engineering
Material reduction
3D printing
Rasulzade, J.
Maksum, Y.
Nogaibayeva, M.
Rustamov, S.
Akhmetov, Bakytzhan
Reduction of material usage in 3D printable structures using topology optimization accelerated with U-Net convolutional neural network
description Today’s 3D printers allow the creation of very advanced structures from various materials, starting from simple plastics up to metal alloys. Since the printing time and amount of material used to create structures are considered very important in terms of cost and energy consumption, it is better to optimize structures for that particular application taking into account all the conditions. In the current work, U-Net convolutional neural network-based topology optimization method (TO) that allows to reduce the material usage and eventually reduces the cost of 3D printing is introduced. The results showed that the accuracy of the method is highly reliable and can be used for designing various 3D printable structures and it applies to any type of materials since properties of materials can be included in TO.
author2 School of Mechanical and Aerospace Engineering
author_facet School of Mechanical and Aerospace Engineering
Rasulzade, J.
Maksum, Y.
Nogaibayeva, M.
Rustamov, S.
Akhmetov, Bakytzhan
format Article
author Rasulzade, J.
Maksum, Y.
Nogaibayeva, M.
Rustamov, S.
Akhmetov, Bakytzhan
author_sort Rasulzade, J.
title Reduction of material usage in 3D printable structures using topology optimization accelerated with U-Net convolutional neural network
title_short Reduction of material usage in 3D printable structures using topology optimization accelerated with U-Net convolutional neural network
title_full Reduction of material usage in 3D printable structures using topology optimization accelerated with U-Net convolutional neural network
title_fullStr Reduction of material usage in 3D printable structures using topology optimization accelerated with U-Net convolutional neural network
title_full_unstemmed Reduction of material usage in 3D printable structures using topology optimization accelerated with U-Net convolutional neural network
title_sort reduction of material usage in 3d printable structures using topology optimization accelerated with u-net convolutional neural network
publishDate 2024
url https://hdl.handle.net/10356/173663
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