Analyzing microstructure relationships in porous copper using a multi-method machine learning-based approach

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Abstract

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.

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Wijaya, A., Wagner, J., Sartory, B., & Brunner, R. (2024). Analyzing microstructure relationships in porous copper using a multi-method machine learning-based approach. Communications Materials, 5(1). https://doi.org/10.1038/s43246-024-00493-5

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