This grant provides funding to develop a modeling, processing, and fabrication protocol for the creation of a structural, multifunctional metal matrix composite designed for both strength and damping effectiveness. Damping refers to a material?s ability to suppress mechanical and acoustic vibrations. It is important as a means to improve material performance, extend a component?s lifetime, and reduce unwanted noise during device operation. Specifically, this study will explore methods to fabricate composites that contain dispersed structural reinforcements that are capable of exhibiting ferroelectric behavior. If properly prepared, when placed within structural metallic materials, such reinforcements can be actuated in ways that will improve a component?s ability to dissipate unwanted vibrations. Implementation and optimization of this beneficial effect will rely on the ability to successfully process microstructural architectures in appropriate phase states. This will necessitate the adaptation of a low temperature electroforming process capable of preserving the ferroelectric and/or piezoelectric characteristics of the reinforcement through manufacturing. Phase field modeling will be employed to provide robust sensitivity- and benefit-analyses that will guide the highly-sensitive processing-structure-property-performance relationships that are possible in these material systems.
The results of this research will provide the manufacturing foundation for new damping-effective structural composites. Additionally, the project will exemplify a successful implementation of the advanced and broader design concept of material multifunctionality, wherein traditionally-passive microstructural constituents are instead incorporated in ways that enable an active (or ?smart?) response to a particular application need. Future development of new multifunctional material systems will most likely proceed in a similar manner; that is, through the innovative development or adaptation of new processing techniques as guided by advanced, physically-faithful, computationally-intensive modeling and predictive methodologies.
