Prediction and validation of the transverse mechanical behavior of unidirectional composites considering interfacial debonding through convolutional neural networks

Kim, Do-Won; Lim, Jae Hyuk; Lee, Seung−Chul

doi:10.1016/j.compositesb.2021.109314

Cited by 56 publications

(18 citation statements)

References 42 publications

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“…Using a convolutional neural network (CNN), Kim et al effectively developed a prediction model of the stress−strain curves of unidirectional composites with complex microstructures, presenting an interesting example. 35 Notably, the factors related to the target performance are diverse; however, the input in previous studies, such as microstructure morphology, is frequently singular. 36 Consequently, the information that the deep-learning framework can capture is limited, necessitating a significant amount of data to maintain an acceptable level of prediction accuracy.…”

Section: Introductionmentioning

confidence: 99%

Multimodal Deep-Learning Framework for Accurate Prediction of Wettability Evolution of Laser-Textured Surfaces

Zhang

Yang

Zhao

et al. 2023

ACS Appl. Mater. Interfaces

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The lengthy process through which laser-textured surfaces transform from hydrophilic to hydrophobic severely restricts their practical applications. Accurately predicting the wettability evolution curve is crucial; however, developing a reliable prediction model remains challenging. Herein, a data-driven multimodal deep-learning framework was developed, in which multimodal data of micro/nanostructure morphology images, composition distribution images, and time information are effectively coupled and fed into a convolutional neural network (CNN). Rich data input and in-depth data mining make the framework more robust, achieving accurate prediction of the wettability evolution curves of various typical micro/nanostructures. Additionally, accurate prediction of input images with varying magnifications and untrained laser-textured surfaces demonstrates the generalizability of the multimodal CNN framework. The visualization results of the convolution layer confirmed the rationality of the information learned by the model. Additionally, the proposed multimodal CNN framework was successfully utilized to investigate the optimization process. Further, a laser-textured surface with a shorter evolution period and a larger final contact angle was realized. The proposed multimodal CNN framework offers an efficient and cost-effective method for predicting the wettability evolution curves and exploring the optimization processes, enhancing the application potential of laser micro/nanofabrication of superhydrophobic surfaces.

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Section: Introductionmentioning

confidence: 99%

Multimodal Deep-Learning Framework for Accurate Prediction of Wettability Evolution of Laser-Textured Surfaces

Zhang

Yang

Zhao

et al. 2023

ACS Appl. Mater. Interfaces

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“…Trained ML can computationally advantage classical physics-based numerical methods in several orders of magnitude [ 26 , 27 , 28 ]. In the geomechanics and materials fields AI (Artificial Intelligence) and ML have shown different levels of success in multiscale problems [ 29 , 30 ], material constitutive modelling [ 31 , 32 , 33 , 34 ] as well as in the study of composite in both forward and inverse design approaches [ 35 , 36 , 37 ]. Some recent applications of AI in the macro modelling of geotechnical problems include: natural hazard prediction and mitigation [ 38 ], determination of driven piles bearing capacity in sands using ANN [ 39 ], advanced ML techniques [ 40 ] and AI systems optimized by evolutionary computation [ 41 ], determination of slope stability with ANN [ 42 ], among others.…”

Section: Introductionmentioning

confidence: 99%

Predicting the Non-Deterministic Response of a Micro-Scale Mechanical Model Using Generative Adversarial Networks

Argilaga

Zhuang

2022

Materials

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Recent improvements in micro-scale material descriptions allow to build increasingly refined multiscale models in geomechanics. This often comes at the expense of computational cost which can eventually become prohibitive. Among other characteristics, the non-determinism of a micro-scale response makes its replacement by a surrogate particularly challenging. Machine Learning (ML) is a promising technique to substitute physics-based models, nevertheless existing ML algorithms for the prediction of material response do not integrate non-determinism in the learning process. Is it possible to use the numerical output of the latest micro-scale descriptions to train a ML algorithm that will then provide a response at a much lower computational cost? A series of ML algorithms with different levels of depth and supervision are trained using a data-driven approach. Gaussian Process Regression (GPR), Self-Organizing Maps (SOM) and Generative Adversarial Networks (GANs) are tested and the latter retained because of its superior results. A modified GANs with lower network depth showed good performance in the generation of failure probability maps, with good reproduction of the non-deterministic micro-scale response. The trained generator can be incorporated into existing multiscale models allowing to, at least partially, bypass the costly micro-scale computations.

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“…[1,2] Instead of using explicit equations to establish the relationship between material structures and properties in a traditional way, some ML algorithms use elements (trees, neurons) to implicitly approximate complex functions. [3][4][5] In addition, ML algorithm like deep neural network (DNN) is a promising alternative to approximate the solution of partial differential equations. [6] ML is a data-driven technique, which learns the structure-property relationships of materials with the data obtained from experiments or numerical simulations.…”

Section: Introductionmentioning

confidence: 99%

Inverse Design of Digital Materials Using Corrected Generative Deep Neural Network and Generative Deep Convolutional Neural Network

Zhi

Chang

et al. 2022

Advanced Intelligent Systems

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Generative networks are effective tools for digital materials (DM) inverse design. However, the optimization performance of generative networks is restricted by the increasing discrepancy between the optimized input and the prescribed input domain as the design loop increases. Herein, a correction technique is incorporated into generative deep neural network (GDNN) and generative deep convolutional neural network (GDCNN). The correction is performed by pulling the machine learning (ML)‐optimized inputs back to the prescribed domain at certain interval during the optimization process instead of only postprocessing at the end. A DM system with two phases, i.e., the matrix phase and the pore phase, is used for the structural design. The datasets are produced using a numerical model and describe the relationship between the material structure and the elastic modulus and tensile strength. The results show that the optimization effectiveness of corrected GDNN/GDCNN significantly improves given the fact that more structures converge to the best structures and fewer nonrepetitive structures are left after optimization, which helps to search for the best structures and decreases the computational burden when verifying the ML‐recommended structures. The corrected GDNN and GDCNN also manage to find structures with higher tensile strength in the new inverse design.

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Prediction and validation of the transverse mechanical behavior of unidirectional composites considering interfacial debonding through convolutional neural networks

Cited by 56 publications

References 42 publications

Multimodal Deep-Learning Framework for Accurate Prediction of Wettability Evolution of Laser-Textured Surfaces

Multimodal Deep-Learning Framework for Accurate Prediction of Wettability Evolution of Laser-Textured Surfaces

Predicting the Non-Deterministic Response of a Micro-Scale Mechanical Model Using Generative Adversarial Networks

Inverse Design of Digital Materials Using Corrected Generative Deep Neural Network and Generative Deep Convolutional Neural Network

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