Advanced deep learning-based 3D microstructural characterization of multiphase metal matrix composites
- The quantitative analysis of microstructural features is a key to understanding the micromechanical behavior of metal matrix composites (MMCs), which is a premise for their use in practice. Herein, a 3D microstructural characterization of a five-phase MMC is performed by synchrotron X-ray computed tomography (SXCT). A workflow for advanced deep learning-based segmentation of all individual phases in SXCT data is shown using a fully convolutional neural network with U-net architecture. High segmentation accuracy is achieved with a small amount of training data. This enables extracting unprecedently precise microstructural parameters (e.g., volume fractions and particle shapes) to be input, e.g., in micromechanical models.
| Author details: | Sergei EvsevleevORCiD, Sidnei PaciornikORCiD, Giovanni BrunoORCiDGND |
|---|---|
| DOI: | https://doi.org/10.1002/adem.201901197 |
| ISSN: | 1438-1656 |
| ISSN: | 1527-2648 |
| Title of parent work (English): | Advanced engineering materials |
| Publisher: | Wiley-VCH |
| Place of publishing: | Weinheim |
| Publication type: | Article |
| Language: | English |
| Date of first publication: | 2020/01/31 |
| Publication year: | 2020 |
| Release date: | 2023/06/08 |
| Tag: | computed tomography; convolutional neural networks; deep learning; matrix composites; metal; segmentations |
| Volume: | 22 |
| Issue: | 4 |
| Article number: | 1901197 |
| Number of pages: | 6 |
| Funding institution: | DFGGerman Research Foundation (DFG)European Commission [BR 5199/3-1] |
| Organizational units: | Mathematisch-Naturwissenschaftliche Fakultät / Institut für Physik und Astronomie |
| DDC classification: | 5 Naturwissenschaften und Mathematik / 53 Physik / 530 Physik |
| Peer review: | Referiert |
| Publishing method: | Open Access / Hybrid Open-Access |
| License (German): |  CC-BY - Namensnennung 4.0 International |
