|About this Abstract
||2020 TMS Annual Meeting & Exhibition
||Algorithm Development in Materials Science and Engineering
||L-7: Machine Learning Driven Functionally Graded Material Designs for Mitigation of Thermally Induced Stress
||Zhizhou Zhang, Zeqing Jin, Kahraman Demir, Grace Gu
|On-Site Speaker (Planned)
High operating temperatures of engineering components can generate stress concentration at sharp corners and reduce fatigue life. Functionally graded materials can redistribute stresses at critical locations and achieve better mitigation effects compared to commonly used corner fillets. However, simulating all the possible material pattern combinations would be intractable. In this work, to extrapolate optimal patterns around the critical areas of interest, we propose using convolutional neural networks (CNN) where the input is a matrix containing material distribution and property information and the output is the stress profile. Training data are generated through finite element analysis simulations and a genetic algorithm is then applied to search for the top designs. Results show that annulus material patterns at a sharp corner can reduce the peak stress 40% more than just using a notch design. The implication of this work is the potential to extend service life of engineering components in extreme environments.
||Planned: Supplemental Proceedings volume