|About this Abstract
||7th World Congress on Integrated Computational Materials Engineering (ICME 2023)
||A Deep Learning Approach for Phase Detection in 2D-XRD Patterns of Ti-6Al-4V
||Weiqi Yue, Pawan Tripathi, Nathaniel Tomczak, Gabriel Ponon, Zhuldyz Ualikhankyzy, Matthew Willard, Vipin Chaudhary, Roger French
|On-Site Speaker (Planned)
Beamline 2-D X-ray diffraction (XRD) diffractograms play an important role in determining properties associated with crystal structure such as crystalline phase, texture, and chemistry. Deep learning techniques help create an efficient and accurate solution capable of processing large amounts of image data. Herein, we analyzed patterns of a Ti-6Al-4V (Ti-64) alloy that was heat treated throughout capture of the diffraction patterns. We designed a convolutional neural network (CNN) to predict the titanium beta phase volume percentage during the temperature fluctuations. Images were pre-processed to remove bias in the XRD patterns and prevent loss of information. The 2-D XRD patterns were input directly into the CNN model instead of traditional 1-D XRD peak patterns. The model was trained using an experimental dataset that contains 3,012 XRD images and was tested with another 1,102 experimental XRD images, achieving a mean square error (MSE) of 0.076%.