Computational Technology Resources - CCC

L. Meng¹, C. Yang^1,2, Y.L. Hou³, T. Gao¹, J.H. Zhu^1,4 and W.H. Zhang¹

¹State IJR Centre of Aerospace Design and Additive Manufacturing Northwestern Polytechnical University, Xi’an, China
²TSMC Nanjing Company Limited Nanjing, China
³School of Mechanical and Power Engineering, Zhengzhou University, Zhengzhou, China
⁴Institute of Intelligence Material and Structure, Unmanned System Technologies Northwestern Polytechnical University, Xi’an, China

doi:10.4203/ccc.3.2.6

L. Meng, C. Yang, Y.L. Hou, T. Gao, J.H. Zhu, W.H. Zhang, "Multi-scale structural design considering cellular connectivity using machine learning approaches", in B.H.V. Topping, J. Kruis, (Editors), "Proceedings of the Fourteenth International Conference on Computational Structures Technology", Civil-Comp Press, Edinburgh, UK, Online volume: CCC 3, Paper 2.6, 2022, doi:10.4203/ccc.3.2.6

Keywords: additive manufacturing, multi-scale, connectivity, GANs, structural optimization.

Abstract

Accompanied by the fast evolution of additive manufacturing, multi-scale porous structures are gaining ever-increasing popularity in high-performance structure design. Given the connectivity requirement imposed on neighbouring cellular microstructures for their successful printing, we propose in the current work a criterion for connectivity evaluation based on Dijkstra’s shortest path algorithm, and a unit cell generation model is established with the aid of the Generative Adversarial Network (GAN) approach. A family of 9 lattice units satisfying a prescribed connectivity condition is subsequently optimized under various load conditions. Lastly, a multi-scale structural optimization design approach is developed under the neural network framework, and the best combination of the a priori optimized lattice units is found. The effectiveness of the proposed protocol is verified on a series of numerical examples considering structural stiffness/toughness.

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Front Page Browse CCC CCP CSETS CTR IJRT Other Authors Search Purchase Guide FAQ Contact us	Civil-Comp Conferences ISSN 2753-3239 CCC: 3 PROCEEDINGS OF THE FOURTEENTH INTERNATIONAL CONFERENCE ON COMPUTATIONAL STRUCTURES TECHNOLOGY Edited by: B.H.V. Topping and J. Kruis Paper 2.6 Multi-scale structural design considering cellular connectivity using machine learning approaches L. Meng¹, C. Yang^1,2, Y.L. Hou³, T. Gao¹, J.H. Zhu^1,4 and W.H. Zhang¹ ¹State IJR Centre of Aerospace Design and Additive Manufacturing Northwestern Polytechnical University, Xi’an, China ²TSMC Nanjing Company Limited Nanjing, China ³School of Mechanical and Power Engineering, Zhengzhou University, Zhengzhou, China ⁴Institute of Intelligence Material and Structure, Unmanned System Technologies Northwestern Polytechnical University, Xi’an, China doi:10.4203/ccc.3.2.6 Full Bibliographic Reference for this paper L. Meng, C. Yang, Y.L. Hou, T. Gao, J.H. Zhu, W.H. Zhang, "Multi-scale structural design considering cellular connectivity using machine learning approaches", in B.H.V. Topping, J. Kruis, (Editors), "Proceedings of the Fourteenth International Conference on Computational Structures Technology", Civil-Comp Press, Edinburgh, UK, Online volume: CCC 3, Paper 2.6, 2022, doi:10.4203/ccc.3.2.6 Keywords: additive manufacturing, multi-scale, connectivity, GANs, structural optimization. Abstract Accompanied by the fast evolution of additive manufacturing, multi-scale porous structures are gaining ever-increasing popularity in high-performance structure design. Given the connectivity requirement imposed on neighbouring cellular microstructures for their successful printing, we propose in the current work a criterion for connectivity evaluation based on Dijkstra’s shortest path algorithm, and a unit cell generation model is established with the aid of the Generative Adversarial Network (GAN) approach. A family of 9 lattice units satisfying a prescribed connectivity condition is subsequently optimized under various load conditions. Lastly, a multi-scale structural optimization design approach is developed under the neural network framework, and the best combination of the a priori optimized lattice units is found. The effectiveness of the proposed protocol is verified on a series of numerical examples considering structural stiffness/toughness. download the full-text of this paper (PDF, 6 pages, 747 Kb) go to the previous paper go to the next paper return to the table of contents return to the volume description
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