High-throughput, algorithmic determination of pore parameters from electron microscopy

Yu, Zhong Xin; Shi, Wei; Zhang, Jun Wei; Wang, Bo; Wang, Yu Jiang; Liang, Yue; Tian, Hao

doi:10.1016/j.commatsci.2019.109216

Cited by 10 publications

(8 citation statements)

References 23 publications

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“…Accurate identification of the void structures enables the accurate calculation of macroscopic ILSS parameters of the composite laminates using a theoretically derived equation. The commonly used image segmentation methods for analyzing optical images include thresholding, 13,14 machine learning, 14‐16 and deep learning 17‐22 . Conventionally, thresholding techniques, which include global and local thresholding, involve the selection of threshold based on the image information and the division of the image based on the gray level.…”

Section: Introductionmentioning

confidence: 99%

Identification of voids and interlaminar shear strengths of polymer‐matrix composites by optical microscopy experiment and deep learning methodology

Luo

Zhang

Lei

et al. 2021

Polymers for Advanced Techs

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The internal void defects induced during the manufacturing process of polymer‐matrix composites can significantly degrade the mechanical properties of the composite, particularly the interlaminar shear strength (ILSS). In this study, we developed an innovative integrated methodology based on a deep learning semantic segmentation algorithm, named DeepLabV3+, and a theoretically driven equation to automatically identify voids in optical images and investigate the relationship between the microscopic voids and macroscopic ILSS parameters of the composite laminates. Results suggest that for the best fine‐tuned DeepLabV3+ framework, the corresponding mean pixel accuracy and intersection over union scores on the testing set were 99.84% and 90.82%, respectively, thereby indicating the potential of the generalized trained model. In addition, detailed experiments revealed that the proposed method can successfully obtain the ILSS values of laminates with different void contents. In addition, the ILSS values of the carbon/epoxy laminates decreased by approximately 27% with an increase in the void content from 0.07% to 3.14%.

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Section: Introductionmentioning

confidence: 99%

Identification of voids and interlaminar shear strengths of polymer‐matrix composites by optical microscopy experiment and deep learning methodology

Luo

Zhang

Lei

et al. 2021

Polymers for Advanced Techs

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“…To improve performance, DNNs have been trained to semantically segment images [301][302][303][304][305][306][307][308] . Semantic segmentation DNNs have been developed for focused ion beam scanning electron microscopy [309][310][311] (FIB-SEM), SEM [311][312][313][314] , STEM 287,315 , and TEM 286,310,311,[316][317][318] . For example, applications of a DNN to semantic segmentation of STEM images of steel are shown in figure 3.…”

Section: Semantic Segmentationmentioning

confidence: 99%

Review: Deep Learning in Electron Microscopy

Ede

2020

Preprint

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Deep learning is transforming most areas of science and technology, including electron microscopy. This review paper offers a practical perspective aimed at developers with limited familiarity. For context, we review popular applications of deep learning in electron microscopy. Following, we discuss hardware and software needed to get started with deep learning and interface with electron microscopes. We then review neural network components, popular architectures, and their optimization. Finally, we discuss future directions of deep learning in electron microscopy.

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“…Traditional trial-and-error experimental methods waste time and energy, so computationally guided experiments have gradually become a research hotspot since the Materials Genome Initiative (MGI) was proposed [1][2]. Among those high-throughput methods, machine learning methods have become the focus of MGI research due to their fast and accurate performance [3][4].…”

Section: Introductionmentioning

confidence: 99%

An Improved Artificial Neural Network Elastic Constant Prediction Method Based on Particle Swarm Optimization

Wei¹,

Wang²,

Li³

2022

J. Phys.: Conf. Ser.

Self Cite

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As machine learning methods keep developing in materials science, using machine learning to mine novel materials has become a research hotspot. For microscopic crystals, the first-principles calculation of elastic constants has always been a conventional research method. However, the first-principles calculation method is time-consuming and laborious, which limits the development of materials. In order to achieve efficient discovery of new materials, this paper proposed an improved artificial neural network (ANN) method to predict the elastic modulus of crystals improved by particle swarm optimization (PSO), which build the PSO-ANN model in use of structures and elastic constants of crystals. Experiments show that the root mean square error (RMSE) can reach within 1GPa. To verify the accuracy of the method for other crystal predictions, the MgZn2 crystal is modelled and calculated by using the first-principles calculation method. Comparing with the prediction method proposed, the average error of the predicted elastic constants is within the range 5.6%.

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High-throughput, algorithmic determination of pore parameters from electron microscopy

Cited by 10 publications

References 23 publications

Identification of voids and interlaminar shear strengths of polymer‐matrix composites by optical microscopy experiment and deep learning methodology

Identification of voids and interlaminar shear strengths of polymer‐matrix composites by optical microscopy experiment and deep learning methodology

Review: Deep Learning in Electron Microscopy

An Improved Artificial Neural Network Elastic Constant Prediction Method Based on Particle Swarm Optimization

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