Pragmatic generative optimization of novel structural lattice metamaterials with machine learning

Garland, Anthony; White, Benjamin; Jensen, Scott C.; Boyce, Brad Lee

doi:10.1016/j.matdes.2021.109632

Cited by 66 publications

(43 citation statements)

References 44 publications

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“…We note that the inverse structural identification analysis conducted herein is restrained within the mechanical performance limits of the multiscale cellular patterns investigated. As such, its scope and functionality need to be clearly separated from free-morphology or full-topology optimization methods [ 49 , 51 ], which are beyond the analysis range and context of the current contribution. Using the genetic algorithm elaborated upon in [ 65 ], the base material modulus

, along with the second

and first

material scale features required for a multiscale lattice pattern, to yield a desirable macroscale performance, can be identified.…”

Section: Neural-network-based Multiscale Metamaterials Forward Modeli...mentioning

confidence: 99%

“…However, their successful training requires an appropriately designed machine learning model architecture [ 41 ], along with the existence of sufficient data for model training and validation to be feasible [ 42 ]. Until now, neural networks have been extensively employed to simulate structure-property-related functions [ 43 , 44 , 45 ], to identify functional relationships [ 46 , 47 , 48 ], as well as to optimize inner structural topologies [ 49 , 50 , 51 , 52 , 53 , 54 ]. Amongst others, spinodoid or curved inner beam architectures have been considered [ 55 , 56 ].…”

Section: Introductionmentioning

confidence: 99%

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Enhanced Cellular Materials through Multiscale, Variable-Section Inner Designs: Mechanical Attributes and Neural Network Modeling

Karathanasopoulos

Rodopoulos

2022

Materials

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In the current work, the mechanical response of multiscale cellular materials with hollow variable-section inner elements is analyzed, combining experimental, numerical and machine learning techniques. At first, the effect of multiscale designs on the macroscale material attributes is quantified as a function of their inner structure. To that scope, analytical, closed-form expressions for the axial and bending inner element-scale stiffness are elaborated. The multiscale metamaterial performance is numerically probed for variable-section, multiscale honeycomb, square and re-entrant star-shaped lattice architectures. It is observed that a substantial normal, bulk and shear specific stiffness increase can be achieved, which differs depending on the upper-scale lattice pattern. Subsequently, extended mechanical datasets are created for the training of machine learning models of the metamaterial performance. Thereupon, neural network (NN) architectures and modeling parameters that can robustly capture the multiscale material response are identified. It is demonstrated that rather low-numerical-cost NN models can assess the complete set of elastic properties with substantial accuracy, providing a direct link between the underlying design parameters and the macroscale metamaterial performance. Moreover, inverse, multi-objective engineering tasks become feasible. It is shown that unified machine-learning-based representation allows for the inverse identification of the inner multiscale structural topology and base material parameters that optimally meet multiple macroscale performance objectives, coupling the NN metamaterial models with genetic algorithm-based optimization schemes.

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, along with the second

and first

material scale features required for a multiscale lattice pattern, to yield a desirable macroscale performance, can be identified.…”

Section: Neural-network-based Multiscale Metamaterials Forward Modeli...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Enhanced Cellular Materials through Multiscale, Variable-Section Inner Designs: Mechanical Attributes and Neural Network Modeling

Karathanasopoulos

Rodopoulos

2022

Materials

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“…The pursuit of deep learning methods for shape data has led to the ability to learn on several geometry representations, including shape descriptors, images, voxels, polycubes, signed distance functions, point clouds, and graphs (see [14,15] for a review). Surrogate models have been trained on images [16,17,18,19,20,21], voxels [22,23] and polycubes [24,25]. Images and voxels suffer from resolution problems and data loss due to rasterization.…”

Section: Surrogate Modeling Without Parametric Design Featuresmentioning

confidence: 99%

Toward Reusable Surrogate Models: Graph-Based Transfer Learning on Trusses

Whalen

Mueller

2021

Journal of Mechanical Design

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Surrogate models are often employed to speed up engineering design optimization; however, they typically require that all training data conform to the same parametrization (e.g. design variables), limiting design freedom and prohibiting the reuse of historical data. In response, this paper proposes Graph-based Surrogate Models (GSMs) for space frame structures. The GSM can accurately predict displacement fields from static loads given the structure's geometry as input, enabling training across multiple parametrizations. GSMs build upon recent advancements in geometric deep learning which have led to the ability to learn on undirected graphs: a natural representation for space frames. To further promote flexible surrogate models, the paper explores transfer learning within the context of engineering design, and demonstrates positive knowledge transfer across data sets of different topologies, complexities, loads and applications, resulting in more flexible and data-efficient surrogate models for space frame structures.

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“…[27,34] Für die Auswahl und Gestaltung von Elementarzellen mit diesen spezifischen Eigenschaften gibt es beispielsweise Ansätze zur Verschachtelung von Einheitszellen, um deren Eigenschaften zu kombinieren [48,49]. Darüber hinaus gibt es Ansätze, die sich mit der automatisierten Generierung von Einheitszellen auf Basis neuronaler Netze beschäftigen, um gezielt Materialeigenschaften zu erreichen [50]. Ein weiterer interessanter Anwendungsfall von programmierbaren Materialeigenschaften, sind Gitter, deren Eigenschaften durch ein Fernfeld beeinflusst werden können und sich somit ferngesteuert verformen lassen.…”

Section: Programmierbare Eigenschaftenunclassified