| Abstract Scope |
Electron Backscatter Diffraction method is widely adopted in metal fields. However, despite the abundant data sources, sufficient analysis covering all features is often absent. Especially with the emerging in-situ techniques, data processing is time-consuming, where access to every bit of data is imperative. In this work, a toolkit is developed with the aim of processing EBSD data automatically & efficiently. Two parts of toolkits are developed with Matlab & Mtex. One is used to correlate two maps, with simple implementation, results will generate within few minutes, indicating the grains correlation between two maps. The other correlates a series of in-situ datasets, making each individual grain became trackable. With the assistance of the toolkits, a large dataset containing pixels, digital information, and grains properties through an in-situ process can be created. Thus, the microfeatures and grains behaviors are studied using novel data science methods, especially machine learning & deep learning. |