Organizer(s) / Affiliation(s): Tony Fast, Drexel University, Baskar Ganapathysubramanian, Iowa State University, Surya R. Kalidindi, Drexel University, Ulrich Prahl, IEHK at RWTH Aachen
Abstract: Integrated Computational Materials Engineering – ICME - as an emerging field in materials science and engineering aims at an integrative description of materials and production processes towards engineering and application of components. ICME has been identified as a transformational discipline for improved competitiveness and national security by the National Research Council in 2008 and a first world congress on ICME took place in July 2011 in Seven Springs, PA. Successful ICME – amongst others - eventually aims at local mechanical properties and life cycle prediction thus requiring substantial contributions from materials mechanics. Linking a variety of models and simulation approaches into one integrated scenario further opens new scientific questions like the influence of process parameters or model coefficients on process and properties results, the stability of process or simulation chains or the influence of error propagation along the simulation chain. Scope of this minisymposium is to discuss topics being especially related to linking different models (material models, process models etc) either bridging multiple scales and/or multiple process steps along the value chain.
As the reach of materials characterization and materials simulation expands, there is an increasing need to fuse these collections of data and extract useful patterns and information. Extending informatics and data-driven science to the realm of mechanics of materials will be strategic to the future progress in development of high performance materials. This mini symposium serves as a forum to present innovative data-driven tools that assist in mining large, rich collections of materials and mechanics data. These tools will provide novel approaches to visualizing large materials datasets, leveraging data for decision based design, and enhancing predictive multiscale models over the entire landscape of materials. We encourage theoretical, experimentally-driven, and computational approaches to material characterization, property prediction, structure evolution and linking process-structure and property.