Computational Technology Resources - CCC

Y. Wang¹, W. Zhang^1,2, S.-K. Youn^1,3 and X Guo^1,2

¹Department of Engineering Mechanics, Dalian University of Technology, Dalian, China
²Ningbo Institute of Dalian University of Technology, Ningbo, China
³Department of Mechanical Engineering, Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea

doi:10.4203/ccc.5.2.4

Y. Wang, W. Zhang, S.-K. Youn, X Guo, "Sketch driven machine-learning based topology optimization", in P. Iványi, J. Logo, B.H.V. Topping, (Editors), "Proceedings of the Sixth International Conference on Soft Computing, Machine Learning and Optimisation in Civil, Structural and Environmental Engineering", Civil-Comp Press, Edinburgh, UK, Online volume: CCC 5, Paper 2.4, 2023, doi:10.4203/ccc.5.2.4

Keywords: sketch driven, topology optimization, machine-learning, neural style transfer, VGG-19 model, hand-drawn sketch.

Abstract

Sketch design plays a very important role in model design. In order to improve the efficiency of existing design models that rely on computer-aided and human experience guidance, this work proposes a sketch driven machine-learning based topology optimization method. It helps designers directly design hand-drawn sketches to obtain topology-optimized structures that conform to sketching experience. The proposed method uses neural style transfer technique, and can compensate for the lack of design experience to obtain optimized structures without the need of multiple computational simulation interactions. Specific structural shapes and design styles according to the design requirements also can be obtained. In contrast to the approach of specifying the undesignable domain and initial layout, similarity constraints between sketches and structures are constructed to quantify the degree of inheritance of different sketches. Both 2D and 3D problems are solved to illustrate the effectiveness of the proposed approach.

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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: 5 PROCEEDINGS OF THE SIXTH INTERNATIONAL CONFERENCE ON SOFT COMPUTING, MACHINE LEARNING AND OPTIMISATION IN CIVIL, STRUCTURAL AND ENVIRONMENTAL ENGINEERING Edited by: P. Iványi, J. Logo and B.H.V. Topping Paper 2.4 Sketch driven machine-learning based topology optimization Y. Wang¹, W. Zhang^1,2, S.-K. Youn^1,3 and X Guo^1,2 ¹Department of Engineering Mechanics, Dalian University of Technology, Dalian, China ²Ningbo Institute of Dalian University of Technology, Ningbo, China ³Department of Mechanical Engineering, Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea doi:10.4203/ccc.5.2.4 Full Bibliographic Reference for this paper Y. Wang, W. Zhang, S.-K. Youn, X Guo, "Sketch driven machine-learning based topology optimization", in P. Iványi, J. Logo, B.H.V. Topping, (Editors), "Proceedings of the Sixth International Conference on Soft Computing, Machine Learning and Optimisation in Civil, Structural and Environmental Engineering", Civil-Comp Press, Edinburgh, UK, Online volume: CCC 5, Paper 2.4, 2023, doi:10.4203/ccc.5.2.4 Keywords: sketch driven, topology optimization, machine-learning, neural style transfer, VGG-19 model, hand-drawn sketch. Abstract Sketch design plays a very important role in model design. In order to improve the efficiency of existing design models that rely on computer-aided and human experience guidance, this work proposes a sketch driven machine-learning based topology optimization method. It helps designers directly design hand-drawn sketches to obtain topology-optimized structures that conform to sketching experience. The proposed method uses neural style transfer technique, and can compensate for the lack of design experience to obtain optimized structures without the need of multiple computational simulation interactions. Specific structural shapes and design styles according to the design requirements also can be obtained. In contrast to the approach of specifying the undesignable domain and initial layout, similarity constraints between sketches and structures are constructed to quantify the degree of inheritance of different sketches. Both 2D and 3D problems are solved to illustrate the effectiveness of the proposed approach. download the full-text of this paper (PDF, 8 pages, 453 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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