Estimation of Local Strain Fields in Two-Phase Elastic Composite Materials Using UNet-Based Deep Learning

Raj, Mayank; Thakre, Sanket; Annabattula, Ratna Kumar; Kanjarla, Anand K.

doi:10.1007/s40192-021-00227-2

Cited by 18 publications

(5 citation statements)

References 36 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For example, U‐Net was utilized to estimate the local strain field in elastic composite materials. [ 32 ] CNN was applied to predict the mechanical properties of fiber‐reinforced composites. [ 33 , 34 , 35 ] Some researchers obtained dataset through numerical simulation based on the multi‐scale hydrogel fracture model, for prediction of the fracture behavior of hydrogel by CNN.…”

Section: Introductionmentioning

confidence: 99%

Mechanical Field Guiding Structure Design Strategy for Meta‐Fiber Reinforced Hydrogel Composites by Deep Learning

Liu,

Zhang,

Liu

et al. 2024

Advanced Science

View full text Add to dashboard Cite

Fiber‐reinforced hydrogel composites are widely employed in many engineering applications, such as drug release, and flexible electronics, with more flexible mechanical properties than pure hydrogel materials. Comparing to the hydrogel strengthened by continuous fiber, the meta‐fiber reinforced hydrogel provides stronger individualized design ability of deformation patterns and tunable stiffness, especially for the elaborate applications in joint, cartilage, and organ. In this paper, a novel structure design strategy based on deep learning algorithm is proposed for hydrogel reinforced by meta‐fiber to achieve targeted mechanical properties, such as stress and displacement fields. A solid mechanic model for meta‐fiber reinforced hydrogel is first developed to construct the dataset of fiber distribution and the corresponding mechanical properties of the composite. Generative adversarial network (GAN) is then trained to characterize the relationship between stress or displacement field, and meta‐fiber distribution. The well‐trained GAN is implemented to design meta‐fiber reinforced hydrogel composite structure under specific operation conditions. The results show that the deep learning method may efficiently predict the structure of the hydrogel composite with satisfied confidence, and has great potential for applications in drug delivery and flexible electronics.

show abstract

Section: Introductionmentioning

confidence: 99%

Mechanical Field Guiding Structure Design Strategy for Meta‐Fiber Reinforced Hydrogel Composites by Deep Learning

Liu,

Zhang,

Liu

et al. 2024

Advanced Science

View full text Add to dashboard Cite

show abstract

“…To tackle these challenges, artificial intelligence (AI) has been extensively employed as an alternative to conventional computational modeling for predicting mechanical responses, including the stress-strain (S-S) curve, 6 complex stress 7,8 or strain fields 9,10 and fracture patterns. 11,12 Moreover, integrating deep learning (DL), a subset of AI, with optimization methods unveils pathways for discovering CM designs with desired properties throughout the configuration space.…”

Section: Introductionmentioning

confidence: 99%

Deep generative spatiotemporal learning for integrating fracture mechanics in composite materials: inverse design, discovery, and optimization

Park,

Lee,

Lee

et al. 2024

Mater. Horiz.

View full text Add to dashboard Cite

show abstract

“…They explained that the construction of NNs helps one train a surrogate model to evaluate the structural performance of many potential frame structures without a considerably slower geometric form-finding procedure (than those associated with models without NNs). Using the big-data-driven ML approaches, Raj et al [60] developed a DL-based algorithm known as UNet to predict the local strain fields in a twophase composite material subjected to a uniaxial tensile load. To reduce the computational cost of determining stress distributions in heterogeneous media using FEA, Feng and Prabhakar [61] employed DL to develop the first-ever difference-based NN frameworks based on engineering and statistics knowledge.…”

Section: Introductionmentioning

confidence: 99%

A Machine Learning-Based Surrogate Finite Element Model for Estimating Dynamic Response of Mechanical Systems

2023

View full text Add to dashboard Cite

An efficient approach for improving the predictive understanding of dynamic mechanical system variability is developed in this work. The approach requires low model assessment time through the fitting of surrogate models. ML-based surrogate algorithms for finite element analysis (FEA) are developed in this study to accelerate FEA and prevent rerunning complex simulations. The research begins with an overview of the recent novelties in ML algorithms applied to finite element (FE) and other physics-based computational schemes. To predict the time-varying response variables, that is, the displacement of a twodimensional truss structure, a surrogate FE technique based on ML algorithms is developed. In this work, several ML regression algorithms, including decision trees (DTs) and deep neural networks, are developed, and their efficacies are compared. In this study, the ML-based surrogate FE models are able to effectively predict the response of the truss structure in two dimensions over the entire structure. Extreme gradientboosting DTs provide more precise outcomes and outperform other ML algorithms.INDEX TERMS Surrogate modeling, finite element analysis, mechanical system analysis, machine learning, artificial neural networks, random forest trees, gradient boosting regression trees, adaptive boosting trees.

show abstract

Estimation of Local Strain Fields in Two-Phase Elastic Composite Materials Using UNet-Based Deep Learning

Cited by 18 publications

References 36 publications

Mechanical Field Guiding Structure Design Strategy for Meta‐Fiber Reinforced Hydrogel Composites by Deep Learning

Mechanical Field Guiding Structure Design Strategy for Meta‐Fiber Reinforced Hydrogel Composites by Deep Learning

Deep generative spatiotemporal learning for integrating fracture mechanics in composite materials: inverse design, discovery, and optimization

A Machine Learning-Based Surrogate Finite Element Model for Estimating Dynamic Response of Mechanical Systems

Contact Info

Product

Resources

About