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
Subjects:	Engineering Material reduction 3D printing
Online Access:	https://hdl.handle.net/10356/173663
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Institution:	Nanyang Technological University
Language:	English

id	sg-ntu-dr.10356-173663
record_format	dspace
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
_version_	1794549308091006976

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

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