Working as a researcher-in-residence during these three weeks, I developed my research interests combining with Citrination tool. This study seeks to using machine learning algorithm through Citrination to establish a “fast-acting reduced-order crystal plasticity model” for polycrystalline material. The system is usually underdetermined when adding more fitting parameters than experiments used to calibrate the model. At the preliminary design stage, it’s important to have an early approximation of mechanical properties and microstructure informatics based on the experimental stress-strain data and EBSD results of samples. The final texture and optimal crystal plasticity model with certain initial/boundary/loading condition can be predicted through Citrination tool through leaning the relationship between the microscale texture deformation development and physical properties. The overreaching goal of this study can be extended to the “data-driven material design tool” used for designing expected microstructures with desired crystal plasticity properties depending on given initial texture, processing techniques and conditions.
Also, I learned how to design training processes and evaluate the quality of data on Citrnation platform. Based on the machine learning results, I need to adjust my dataset for better predictions and analyze the theoretical issues might existed in my training dataset. Also, I did some exercise based on “learn-citrinaition”, such as writing PIF from computational calculations, experimental designing for optimization problem, batched properties prediction using queries, etc.
–Mengfei Yuan, Ohio State University