Deep Learning: New Engine for the Study of Material Microstructures and Physical Properties

段素青,

doi:10.12677/mp.2019.96026

2019

DOI: 10.12677/mp.2019.96026

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Deep Learning: New Engine for the Study of Material Microstructures and Physical Properties

段素青段素青¹

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2024

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Adaptive residual convolutional neural network for compressive strength prediction of energetic materials using SEM images

Song,

Yin,

Chen

et al. 2024

Phys. Scr.

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The microstructure of materials has a great influence on the macroscopic mechanical properties of materials, and the relationship between them is of great significance to the design of materials. With the development of artificial intelligence and deep learning, many researchers have used convolutional neural networks(CNN) to correlate microscopic images with material properties, and achieved good results. However, for different types of material performance prediction tasks and datasets of different sizes, researchers need to design a special CNN network structure, and the adjustment steps of network structure parameters are cumbersome and time consuming. In this paper, an adaptive residual convolutional neural network with variable depth and width is proposed. We take the prediction error of the test dataset of the specific material performance prediction task as the evaluation index, and adaptively adjusts the network structure based on simulated annealing algorithm to find the best network structure. Finally, it is verified on the dataset composed of SEM images and compressive strength of polymer explosive materials. MAPE of our model is 1.3% and the goodness of fit is 0.943. Further, we use Grad-CAM to explore the correlation between material microstructure and material properties.

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Adaptive residual convolutional neural network for compressive strength prediction of energetic materials using SEM images

Song,

Yin,

Chen

et al. 2024

Phys. Scr.

View full text Add to dashboard Cite

show abstract

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Deep Learning: New Engine for the Study of Material Microstructures and Physical Properties

Cited by 1 publication

References 67 publications

Adaptive residual convolutional neural network for compressive strength prediction of energetic materials using SEM images

Adaptive residual convolutional neural network for compressive strength prediction of energetic materials using SEM images

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