The approach envisages using physics based models, empirical models and human expertise in an integrated manner to significantly reduce the time and cost of development of new materials and their manufacturing processes.
The properties of a material (such as strength, hardness, fatigue life) depend on its microstructure. The microstructure of a material in turn depends on its chemical composition and the manufacturing processes it is subjected to. These relationships are not well understood. While there are physics based models for predicting what microstructure comes out of some of the manufacturing processes, there are no such models for predicting what properties come out of a given microstructure. Often the only option left is to mine past experimental data and construct approximate models. These models can then be used for predicting what materials and processes might satisfy a given set of property requirements. One can then use process simulation to try out these short-listed materials and processes, and select the ones that best meet the requirements. All this will not be possible without a comprehensive IT platform.
The platform should support among other things:
- Building of a comprehensive knowledge repository on materials and processes
- Collection and integration of data from a variety of sources
- Data mining and model learning
- Knowledge services for material selection
- Process design
- Process simulation
This group is headed by Sreedhar Reddy