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An AI-Assisted Design Method for Topology Optimization without Pre-Optimized Training Data
Published online by Cambridge University Press: 26 May 2022
Abstract
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Engineers widely use topology optimization during the initial process of product development to obtain a first possible geometry design. The state-of-the-art method is iterative calculation, which requires both time and computational power. This paper proposes an AI-assisted design method for topology optimization, which does not require any optimized data. The presented AI-assisted design procedure generates geometries that are similar to those of conventional topology optimizers, but require only a fraction of the computational effort.
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This is an Open Access article, distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives licence (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is unaltered and is properly cited. The written permission of Cambridge University Press must be obtained for commercial re-use or in order to create a derivative work.- Copyright
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References
Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., et al. . (2015), “TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems”, ArXiv:1603.04467 [Cs], available at: https://www.tensorflow.org/.Google Scholar
Abueidda, D.W., Koric, S. and Sobh, N.A. (2020), “Topology optimization of 2D structures with nonlinearities using deep learning”, Computers & Structures, Vol. 237, p. 106283.CrossRefGoogle Scholar
Andreassen, E., Clausen, A., Schevenels, M., Lazarov, B.S. and Sigmund, O. (2011), “Efficient topology optimization in MATLAB using 88 lines of code”, Structural and Multidisciplinary Optimization, Vol. 43 No. 1, pp. 1–16.Google Scholar
Ates, G.C. and Gorguluarslan, R.M. (2021), “Two-stage convolutional encoder-decoder network to improve the performance and reliability of deep learning models for topology optimization”, Structural and Multidisciplinary Optimization, available at:10.1007/s00158-020-02788-w.Google Scholar
Basheer, I.A. and Hajmeer, M. (2000), “Artificial neural networks: fundamentals, computing, design, and application”, Journal of Microbiological Methods, Vol. 43 No. 1, pp. 3–31.CrossRefGoogle ScholarPubMed
Baydin, A.G., Pearlmutter, B.A., Radul, A.A. and Siskind, J.M. (2015), “Automatic differentiation in machine learning: a survey”, ArXiv:1502.05767 [Cs, Stat], available at: http://arxiv.org/abs/1502.05767 (accessed 23 September 2019).Google Scholar
Behzadi, M.M. and Ilies, H.T. (2021), “GANTL: Towards Practical and Real-Time Topology Optimization with Conditional GANs and Transfer Learning”, Journal of Mechanical Design, pp. 1–32.CrossRefGoogle Scholar
Bendsøe, M.P. and Sigmund, O. (2003), Topology Optimization: Theory, Methods, and Applications, Springer, Berlin; Heidelberg; New York.Google Scholar
Chi, H., Zhang, Y., Tang, T.L.E., Mirabella, L., Dalloro, L., Song, L. and Paulino, G.H. (2021), “Universal machine learning for topology optimization”, Computer Methods in Applied Mechanics and Engineering, Vol. 375, p. 112739.CrossRefGoogle Scholar
Díaz, A. and Sigmund, O. (1995), “Checkerboard patterns in layout optimization”, Structural Optimization, Vol. 10 No. 1, pp. 40–45.CrossRefGoogle Scholar
Goodfellow, I., Bengio, Y. and Courville, A. (2016), Deep Learning, The MIT Press, Cambridge, Massachusetts, available at: http://www.deeplearningbook.org.Google Scholar
He, K., Zhang, X., Ren, S. and Sun, J. (2016), “Deep Residual Learning for Image Recognition”, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), presented at the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 770–778.Google Scholar
James, G., Witten, D., Hastie, T. and Tibshirani, R. (2021), An Introduction to Statistical Learning: With Applications in R, Springer US, New York, NY, available at:10.1007/978-1-0716-1418-1.Google Scholar
Kallioras, N.Ath., Kazakis, G. and Lagaros, N.D. (2020), “Accelerated topology optimization by means of deep learning”, Structural and Multidisciplinary Optimization, Vol. 62 No. 3, pp. 1185–1212.CrossRefGoogle Scholar
Karayiannis, N.B. and Venetsanopoulos, A.N. (1993), Artificial Neural Networks: Learning Algorithms, Performance Evaluation, and Applications, Kluwer Academic, Boston.CrossRefGoogle Scholar
Kingma, D.P. and Ba, J. (2017), “Adam: A Method for Stochastic Optimization”, ArXiv:1412.6980 [Cs], available at: http://arxiv.org/abs/1412.6980 (accessed 12 February 2020).Google Scholar
Lee, E. (2012), Stress-Constrained Structural Topology Optimization with Design-Dependent Loads, University of Toronto, September, available at: https://tspace.library.utoronto.ca/handle/1807/32254.Google Scholar
Malviya, M. (2020), A Systematic Study of Deep Generative Models for Rapid Topology Optimization, preprint, engrXiv, available at:10.31224/osf.io/9gvqs.Google Scholar
Mohammed, M., Khan, Muhammad Badruddin, and Bashier, Eihab Bashier Mohammed. (2016), Machine Learning: Algorithms and Applications, CRC Press, available at:10.1201/9781315371658.Google Scholar
Nicolas, P.R. (2017), Scala for Machine Learning - Second Edition, Packt Publishing, Limited, Birmingham, UNITED KINGDOM, available at: http://ebookcentral.proquest.com/lib/tuchemnitz/detail.action?docID=5061334.Google Scholar
Nie, Z., Lin, T., Jiang, H. and Kara, L.B. (2021), “TopologyGAN: Topology Optimization Using Generative Adversarial Networks Based on Physical Fields Over the Initial Domain”, Journal of Mechanical Design, Vol. 143 No. 3, p. 031715.Google Scholar
Picelli, R., Townsend, S., Brampton, C., Norato, J. and Kim, H.A. (2018), “Stress-based shape and topology optimization with the level set method”, Computer Methods in Applied Mechanics and Engineering, Vol. 329, pp. 1–23.CrossRefGoogle Scholar
Qian, C. and Ye, W. (2020), “Accelerating gradient-based topology optimization design with dual-model artificial neural networks”, Structural and Multidisciplinary Optimization, available at:10.1007/s00158-020-02770-6.Google Scholar
Rawat, S. and Shen, M.-H.H. (2019), “A Novel Topology Optimization Approach using Conditional Deep Learning”, ArXiv:1901.04859 [Cs, Stat].Google Scholar
Sigmund, O. (2001), “A 99 line topology optimization code written in Matlab”, Structural and Multidisciplinary Optimization, Vol. 21 No. 2, pp. 120–127.Google Scholar
Sigmund, O. and Maute, K. (2013), “Topology optimization approaches: A comparative review”, Structural and Multidisciplinary Optimization, Vol. 48 No. 6, pp. 1031–1055.Google Scholar
Sigmund, O. and Petersson, J. (1998), “Numerical instabilities in topology optimization: A survey on procedures dealing with checkerboards, mesh-dependencies and local minima”, Structural Optimization, Vol. 16 No. 1, pp. 68–75.Google Scholar
Sosnovik, I. and Oseledets, I. (2017), “Neural networks for topology optimization”, ArXiv:1709.09578 [Cs, Math], available at: (accessed 11 May 2020).Google Scholar
Yamasaki, S., Yaji, K. and Fujita, K. (2021), “Data-driven topology design using a deep generative model”, ArXiv:2006.04559 [Physics, Stat].Google Scholar
Yu, Y., Hur, T., Jung, J. and Jang, I.G. (2019), “Deep learning for determining a near-optimal topological design without any iteration”, Structural and Multidisciplinary Optimization, Vol. 59 No. 3, pp. 787–799.Google Scholar
Zhang, Y., Chen, A., Peng, B., Zhou, X. and Wang, D. (2019), “A deep Convolutional Neural Network for topology optimization with strong generalization ability”, ArXiv:1901.07761 [Cs, Stat].Google Scholar
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