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Analyzing microstructure relationships in porous copper using a multi-method machine learning-based approach
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DOI：10.1038/s43246-024-00493-5.png)
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摘要
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The prediction of material properties from a given microstructure and its reverse engineering displays an essential ingredient for accelerated material design. However, a comprehensive methodology to uncover the processing-structure-property relationship is still lacking. Herein, we develop a methodology capable of understanding this relationship for differently processed porous materials. We utilize a multi-method machine learning approach incorporating tomographic image data acquisition, segmentation, microstructure feature extraction, feature importance analysis and synthetic microstructure reconstruction. Enhanced segmentation with an accuracy of about 95% based on an efficient annotation technique provides the basis for accurate microstructure quantification, prediction and understanding of the correlation of the extracted microstructure features and electrical conductivity. We show that a diffusion probabilistic model superior to a generative adversarial network model, provides synthetic microstructure images including physical information in agreement with real data, an essential step to predicting properties of unseen conditions. Material properties prediction from a given microstructure is important for accelerated design but a comprehensive methodology is lacking. Here, a multi-method machine learning approach is utilized to understand the processing-structure-property relationship for differently processed porous materials.
Keyword:
REGRESSION ANALYSIS
RECONSTRUCTION
SEGMENTATION
FIB/SEM
期刊
C
IF:
9.6
论文数:
1.4K
被引数:
4.3K
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