TOaCNN: Adaptive Convolutional Neural Network for Multidisciplinary Topology Optimization (2310.02069v1)
Abstract: This paper presents an adaptive convolutional neural network (CNN) architecture that can automate diverse topology optimization (TO) problems having different underlying physics. The architecture uses the encoder-decoder networks with dense layers in the middle which includes an additional adaptive layer to capture complex geometrical features. The network is trained using the dataset obtained from the three open-source TO codes involving different physics. The robustness and success of the presented adaptive CNN are demonstrated on compliance minimization problems with constant and design-dependent loads and material bulk modulus optimization. The architecture takes the user's input of the volume fraction. It instantly generates optimized designs resembling their counterparts obtained via open-source TO codes with negligible performance and volume fraction error.
- O. Sigmund and K. Maute, “Topology optimization approaches: A comparative review,” Structural and multidisciplinary optimization, vol. 48, no. 6, pp. 1031–1055, 2013.
- M. Langelaar, “Topology optimization of 3d self-supporting structures for additive manufacturing,” Additive manufacturing, vol. 12, pp. 60–70, 2016.
- W.-h. Choi, J.-m. Kim, and G.-J. Park, “Comparison study of some commercial structural optimization software systems,” Structural and multidisciplinary optimization, vol. 54, no. 3, pp. 685–699, 2016.
- P. Kumar, J. S. Frouws, and M. Langelaar, “Topology optimization of fluidic pressure-loaded structures and compliant mechanisms using the Darcy method,” Structural and Multidisciplinary Optimization, vol. 61, pp. 1637–1655, 2020.
- P. Kumar, “Topology optimization of stiff structures under self-weight for given volume using a smooth Heaviside function,” Structural and Multidisciplinary Optimization, vol. 65, no. 4, p. 128, 2022.
- I. Sosnovik and I. Oseledets, “Neural networks for topology optimization,” Russian Journal of Numerical Analysis and Mathematical Modelling, vol. 34, no. 4, pp. 215–223, 2019.
- S. Banga, H. Gehani, S. Bhilare, S. Patel, and L. Kara, “3D topology optimization using convolutional neural networks,” arXiv preprint arXiv:1808.07440, 2018.
- B. Harish, K. Eswara Sai Kumar, and B. Srinivasan, “Topology optimization using convolutional neural network,” in Advances in Multidisciplinary Analysis and Optimization: Proceedings of the 2nd National Conference on Multidisciplinary Analysis and Optimization, pp. 301–307, Springer, 2020.
- A. Chandrasekhar and K. Suresh, “TOuNN: Topology optimization using neural networks,” Structural and Multidisciplinary Optimization, vol. 63, pp. 1135–1149, 2021.
- D. Wang, C. Xiang, Y. Pan, A. Chen, X. Zhou, and Y. Zhang, “A deep convolutional neural network for topology optimization with perceptible generalization ability,” Engineering Optimization, vol. 54, no. 6, pp. 973–988, 2022.
- E. Andreassen, A. Clausen, M. Schevenels, B. S. Lazarov, and O. Sigmund, “Efficient topology optimization in matlab using 88 lines of code,” Structural and Multidisciplinary Optimization, vol. 43, pp. 1–16, 2011.
- P. Kumar, “TOPress: a MATLAB implementation for topology optimization of structures subjected to design-dependent pressure loads,” Structural and Multidisciplinary Optimization, vol. 66, no. 4, 2023.
- L. Xia and P. Breitkopf, “Design of materials using topology optimization and energy-based homogenization approach in matlab,” Structural and multidisciplinary optimization, vol. 52, no. 6, pp. 1229–1241, 2015.
- P. Kumar and M. Langelaar, “On topology optimization of design-dependent pressure-loaded three-dimensional structures and compliant mechanisms,” International Journal for Numerical Methods in Engineering, vol. 122, no. 9, pp. 2205–2220, 2021.
- Khaish Singh Chadha (3 papers)
- Prabhat Kumar (46 papers)