Deep learning driven real time topology optimisation based on initial stress learning

Yan, Jun; Zhang, Qi; Xu, Qi; Fan, Zhirui; Li, Haijiang; Sun, Wei; Wang, Guangyuan

doi:10.1016/j.aei.2021.101472

Cited by 29 publications

(11 citation statements)

References 40 publications

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“…The network presented in this study also performed well when compared to more advanced network architectures such as convolutional neural networks for topologically optimised truss structures that achieved voxel value errors of 5.63% [33]. Comparison with further works that developed machine learned structural design models was not possible for studies which reported performance with non-percentage based metrics such as MAE [29] or MSE [31,32,47].…”

Section: Other Neural Network Performance Observationsmentioning

confidence: 85%

“…(c) ML inverse operators: machine learned components which solve the inverse problem (structural design) by mapping a set of structural utilisations and known priors to model parameters directly. Examples include estimating cross-sectional properties for simple trusses directly based on known optimum examples using neural networks (1994) [29] and approximating topological optimised structures in real-time using convolutional neural networks (2022) [47].…”

Section: A Novel Perspectivementioning

confidence: 99%

“…In this respect, machine learned optimisation solvers and inverse operators are less common. Furthermore, the machine learned forward operators and optimisation solvers identified above typically require some form of iteration to achieve structural design; machine learned inverse operators on the other hand can be non-iterative [29,47]. The ability to provide real-time design feedback is of particular interest to address the limitations of current iterative structural design approaches.…”

Section: A Novel Perspectivementioning

confidence: 99%

See 2 more Smart Citations

Machine learning for structural design models of continuous beam systems via influence zones

Gallet,

Liew,

Hajirasouliha

et al. 2024

Inverse Problems

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This work develops a machine learned structural design model for continuous beam systems from the inverse problem perspective. After demarcating between forward, optimisation and inverse machine learned operators, the investigation proposes a novel methodology based on the recently developed influence zone concept which represents a fundamental shift in approach compared to traditional structural design methods. The aim of this approach is to conceptualise a non-iterative structural design model that predicts cross-section requirements for continuous beam systems of arbitrary system size. After generating a dataset of known solutions, an appropriate neural network architecture is identified, trained, and tested against unseen data. The results show a mean absolute percentage testing error of 1.6% for cross-section property predictions, along with a good ability of the neural network to generalise well to structural systems of variable size. The CBeamXP dataset generated in this work and an associated python-based neural network training script are available at an open-source data repository to allow for the reproducibility of results and to encourage further investigations.

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Section: Other Neural Network Performance Observationsmentioning

confidence: 85%

Section: A Novel Perspectivementioning

confidence: 99%

Section: A Novel Perspectivementioning

confidence: 99%

See 1 more Smart Citation

Machine learning for structural design models of continuous beam systems via influence zones

Gallet,

Liew,

Hajirasouliha

et al. 2024

Inverse Problems

View full text Add to dashboard Cite

show abstract

“…The deep learning models presented in this paper promise both rapid and precise predictions, crucial in scenarios like real-time structural assessments [119,120] or during novel manufacturing processes [48,49]. The authors are actively working on the application of these models for digital twin-based high-throughput modeling.…”

Section: Future Work and Limitationsmentioning

confidence: 99%

Prediction of 4D stress field evolution around additive manufacturing-induced porosity through progressive deep-learning frameworks

Rezasefat,

Hogan

2024

Mach. Learn.: Sci. Technol.

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This study investigates the application of machine learning models to predict time-evolving stress fields in complex three-dimensional structures trained with full-scale finite element simulation data. Two novel architectures, the Multi-Decoder CNN (MUDE-CNN) and the Multiple Encoder-Decoder Model with Transfer Learning (MTED-TL), were introduced to address the challenge of predicting the progressive and spatial evolutional of stress distributions around defects. The MUDE-CNN leveraged a shared encoder for simultaneous feature extraction and employed multiple decoders for distinct time frame predictions, while MTED-TL progressively transferred knowledge from one encoder-decoder block to another, thereby enhancing prediction accuracy through transfer learning. These models were evaluated to assess their accuracy, with a particular focus on predicting temporal stress fields around an additive manufacturing-induced isolated pore, as understanding such defects is crucial for assessing mechanical properties and structural integrity in materials and components fabricated via additive manufacturing. The temporal model evaluation demonstrated MTED-TL's consistent superiority over MUDE-CNN, owing to transfer learning's advantageous initialization of weights and smooth loss curves. Furthermore, an autoregressive training framework was introduced to improve temporal predictions, consistently outperforming both MUDE-CNN and MTED-TL. By accurately predicting temporal stress fields around AM-induced defects, these models can enable real-time monitoring and proactive defect mitigation during the fabrication process. This capability ensures enhanced component quality and enhances the overall reliability of additively manufactured parts.

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“…Nie et al [9] proposed a new method for topology optimization using generative adversarial networks, leveraging physical fields over the initial design domain to generate high-quality optimized structures. In the recent contribution of Yan et al [10], a new method for real-time topology optimization driven by deep learning is introduced, by utilizing initial stress learning to achieve efficient and accurate optimization, marking a significant step forward in the development of intelligent design systems.…”

Section: Open Access Edited Bymentioning

confidence: 99%

Isogeometric multi-patch topology optimization based on pix2pix

Meng

You

2023

Front. Phys.

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We present a novel approach that combines the power of pix2pix, an image-to-image translation framework, with the advanced capabilities of isogeometric multi-patch analysis for topology optimization. The proposed method adds the Nitsche’s methods into the advantages of Isogeometric analysis (IGA), thus gaining the ability to handle complex geometries by generating locally smooth and well-converged results. Additionally, the usage of generative adversarial network based pix2pix allows for a more efficient representation of the design space, reducing the computational cost of the optimization process. This approach has shown promising results in various numerical examples. This technique aims to improve the efficiency of conceptual design in complex engineering applications.

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Deep learning driven real time topology optimisation based on initial stress learning

Cited by 29 publications

References 40 publications

Machine learning for structural design models of continuous beam systems via influence zones

Machine learning for structural design models of continuous beam systems via influence zones

Prediction of 4D stress field evolution around additive manufacturing-induced porosity through progressive deep-learning frameworks

Isogeometric multi-patch topology optimization based on pix2pix

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