Advanced Computational Techniques for Materials - by - Design Afosr Contract

Email: [email protected], Tel: (607) 255 – 9104 URL: http://mpdc.mae.cornell.edu/ Abstract The objective of this work is to address a number of mathematical and computational issues critical to the development of a robust multi-length scale and multi-stage deformation process design simulator for the control of microstructure-sensitive properties in aircraft manufacturing applications. As part of the research effort, multiple technical developments are being accomplished. An efficient framework for accurately assessing the effect of uncertainty in process and material parameters, in initial conditions and in the microstructure has been developed. A spectral polynomial chaos framework as well as a novel support space method have been developed for analyzing uncertainty in metal forming problems. On the meso-scale, robust statistical learning techniques as well as gradient based methods have been formulated for process sequence selection and design of highly optimized synthetic microstructures. Maximum entropy concepts have been used to develop an algorithm for efficient reconstruction of microstructure classes using a limited number of microstructure realizations. It is strongly believed that these advanced techniques can drastically improve process and material predictions in critical components.