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...
| Main Authors: | , , , , |
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| Other Authors: | |
| Format: | Journal Article |
| Language: | English |
| Published: |
2024
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| Online Access: | https://hdl.handle.net/10356/173663 |
| _version_ | 1826113217926004736 |
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| author | Rasulzade, J. Maksum, Y. Nogaibayeva, M. Rustamov, S. Akhmetov, Bakytzhan |
| 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 |
| author_sort | Rasulzade, J. |
| collection | NTU |
| 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. |
| first_indexed | 2024-10-01T03:19:55Z |
| format | Journal Article |
| id | ntu-10356/173663 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T03:19:55Z |
| publishDate | 2024 |
| record_format | dspace |
| spelling | ntu-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 |
| 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 |
| title | 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_short | 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 |
| topic | Engineering Material reduction 3D printing |
| url | https://hdl.handle.net/10356/173663 |
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