Reduction of Material Usage in 3D Printable Structures Using Topology Optimization Accelerated with U-Net Convolutional Neural Network

Authors

  • J. Rasulzade Department of Computer Science and Information Sciences, ADA University, 61 Ahmadbey Aghaoglu str., Baku, 1008, Azerbaijan
  • Y. Maksum Department of Mechanical Engineering, Satbayev University, 22 Satbaev str., Almaty, 050013, Kazakhstan; Department of Chemical Engineering, University of Birmingham, Birmingham, B15 2TT, UK
  • M. Nogaibayeva Department of Mechanical Engineering, Satbayev University, 22 Satbaev str., Almaty, 050013, Kazakhstan
  • S. Rustamov Department of Computer Science and Information Sciences, ADA University, 61 Ahmadbey Aghaoglu str., Baku, 1008, Azerbaijan
  • B. Akhmetov Department of Mechanical Engineering, Satbayev University, 22 Satbaev str., Almaty, 050013, Kazakhstan; Department of Mechanical and Aerospace Engineering, Nanyang Technological University, 50 Nanyang Ave., 639798, Singapore

DOI:

https://doi.org/10.18321/ectj1471

Keywords:

Material reduction, 3D printing, Topology optimization, Convolutional neural network

Abstract

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.

References

(1). M. Shams, Z. Mansurov, C. Daulbayev, B. Bakbolat, Eurasian Chem.-Technol. J. 23 (2021) 257–266. Crossref

(2). J.N. Levesque, A Shah, S Ekhtiari, J.R. Yan, et al., EFORT Open Reviews 5 (2020) 430–441. Crossref

(3). O. Sigmund, K. Maute, Struct. Multidis. Optim. 48 (2013) 1031–1055. Crossref

(4). J. Martínez-Frutos, D. Herrero-Pérez, Comput. Methods Appl. Mech. Eng. 311 (2016) 393–414. Crossref

(5). T. Borrvall, J. Petersson, Comput. Methods Appl. Mech. Eng. 190 (2001) 6201–6229. Crossref

(6). A. Mahdavi, R. Balaji, M. Frecker, E.M. Mockensturm, Struct. Multidisc. Optim. 32 (2006) 121–132. Crossref

(7). K. Vemaganti, W.E. Lawrence, Comput. Methods Appl. Mech. Eng. 194 (2005) 3637–3667. Crossref

(8). S. Schmidt, V. Schulz, Comput. Visial Sci. 14 (2011) 249–256. Crossref

(9). K. Suresh, Struct. Multidisc. Optim. 42 (2010) 665–679. Crossref

(10). T. Zegard, G.H. Paulino, Struct. Multidisc. Optim. 48 (2013) 473–485. Crossref

(11). K. Paraskevoudis, P. Karayannis, E.P. Koumoulos, Processes 8 (2020) 1464. Crossref

(12). N.S. Johnson, P.S. Vulimiri, A.C. To, X. Zhang, et al., Addit. Manuf. 36 (2020) 101641. Crossref

(13). K. Guo, Z. Yang, C.-H. Yu, M.J. Buehler, Mater. Horiz. 4 (2021). Crossref

(14). I. Sosnovik, I. Oseledets, Russ. J. Numer. Anal. Math. Model. 54 (2019) 215–223. Crossref

(15). D. Wang, C. Xiang, Y. Pan, A. Chen, et al., Eng. Optim. 54 (2022) 973–988. Crossref

(16). Z. Nie, T. Lin, H. Jiang, L.B. Kara, J. Mech. Des. 143 (2021) 031715. Crossref

(17). M.P. Bendsøe, Struct. Optim. 1 (1989) 193–202. Crossref

(18). G.I.N. Rozvany, Struct. Multidisc. Optim. 37 (2009) 217–237. Crossref

(19). T.P. Ribeiro, L.F.A. Bernardo, J.M.A. Andrade, Appl. Sci. 11 (2021) 2112. Crossref

(20). D.J. Munk, G.A. Vio, G.P. Steven, Struct. Multidisc. Optim. 52 (2015) 613–631. Crossref

(21). J.D. Deaton, R.V. Grandhi, Struct. Multidisc. Optim. 49 (2014) 1–38. Crossref

(22). O. Sigmund, Struct. Multidisc. Optim. 21 (2001) 120–127. Crossref

(23). K. Svanberg, Int. J. Numer. Methods. Eng. 24 (1987) 359–373. Crossref

(24). C. Lundgaard, J. Alexandersen, M. Zhou, C. Schousboe, et al., Struct. Multidisc. Optim. 58 (2018) 969–95. Crossref

(25). T. Dbouk, Appl. Therm. Eng. 112 (2017) 841–854. Crossref

(26). E. Wadbro, M. Berggren, SIAM Review 51 (2009). Crossref

(27). A. Neofytou, F. Yu, L. Zhang, H.A. Kim. Level Set Topology Optimization for Fluid-Structure Interactions. AIAA 2021-1686. Session: Shape and Topology Optimization I, 2021. Crossref

(28). T. Gao, P. Xu, W. Zhang, Comput. Struct. 173 (2016) 150–160. Crossref

(29). F.J. Ramírez-Gil, E.C.N. Silva, W. Montealegre-Rubio, Comput. Methods Appl. Mech. Eng. 302 (2016) 44–69. Crossref

(30). F.J. Ramírez-Gil, C.M. Pérez-Madrid, E.C.N. Silva, W. Montealegre-Rubio, Sustain. Comput. Informatics Syst. 30 (2021) 100481. Crossref

(31). D. Jiang, R. Hoglund, D.E. Smith, Fibers 7 (2019). Crossref

(32). W. Hunter, et al., (2017), Topy - topology optimization with python, Crossref

Downloads

Published

2022-12-12

How to Cite

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://doi.org/10.18321/ectj1471

Issue

Vol. 24 No. 4 (2022)

Section

Articles

License

Copyright (c) 2022 Eurasian Chemico-Technological Journal

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

You are free to: Share — copy and redistribute the material in any medium or format. Adapt — remix, transform, and build upon the material for any purpose, even commercially.

Eurasian Chemico-Technological Journal applies a Creative Commons Attribution 4.0 International License to articles and other works we publish.

Subject to the acceptance of the Article for publication in the Eurasian Chemico-Technological Journal, the Author(s) agrees to grant Eurasian Chemico-Technological Journal permission to publish the unpublished and original Article and all associated supplemental material under the Creative Commons Attribution 4.0 International license (CC BY 4.0).

Further distribution of this work must maintain attribution to the author(s) and the published article’s title, journal citation, and DOI.