Optimization of Multi-Scale Microstructure Design to Improve Macro-Level Engineering Performance in Aerospace Applications
CHALLENGE - Microstructure optimization is important for improving the performance of critical components in numerous aerospace applications. The present work addresses several microstructure design optimization applications such as an airframe thermal buckling problem, plastic deformation and vibration tuning problems for cantilever beams made of alpha-Titanium and Galfenol. The goal of these optimization problems is to improve various macrostructural performance criteria by identifying the best microstructure designs.
SOLUTION - In this work, the microstructure is quantified with the orientation distribution function (ODF). The ODF of polycrystalline alloys is represented in a discrete finite element form. The homogenized properties are computed through the ODF. The inverse problem of identifying optimal ODFs that leads to desired combinations of properties is then solved through the use of an efficient sampling algorithm and a genetic optimization method. The optimization is performed implementing Incremental Space Filter and Latin Hypercube Sampling to Non-Dominated Sorting Genetic Algorithm (NSGA-II).
BENEFITS - Single crystal and multiple optimum polycrystal solutions were identified and a significant improvement in macro design objectives was achieved.