Details

Autor(en) / Beteiligte

• Wijaya, Andi
• Wagner, Julian
• Sartory, Bernhard
• Brunner, Roland

Titel

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

Ist Teil von

• Communications materials, 2024-12, Vol.5 (1), p.59-13

Ort / Verlag

London: Nature Publishing Group

Erscheinungsjahr

2024

Link zum Volltext

Quelle

EZB Electronic Journals Library

Beschreibungen/Notizen

• 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.