Rheo-structurally informed design of AM processes for gradient materials
Abstract
Rheo-structurally informed design of AM processes for gradient materialsAdditive Manufacturing (AM) processes have revolutionized the way we think about materials and structures, and specifically ones that are application-specific to DoD capabilities. Whether oneis interested in designing advanced components consisting of ceramics of different geometries, or large-scale structural componentsprinted from soil and other indigenous resources, AM processes offer a wide spectrum of pathways to materials# accelerated design and production. However, a major shortcoming of virtually all additively manufactured components is lack of control over micro and mesostructural evolutions that ultimately govern the macroscopic properties of the printed components. Gradient materials, in which the sub-structures are deliberately designed and embedded, present a paradigm shift in material design. By targeted design of the hierarchical structures within a system, one can introduce new functionalities that are only possible through a bottom-up approach. In doing so, mesoscale holds the key to a dynamical control over structural features of a gradient material. At micro scale, individual component interactions (ex. polymeric systems, ceramic particulates such as Silicon Carbide) govern the mesostructure, and at the macroscale collective behavior of mesoscale features determine overall mechanical, thermal, or electrical properties of the printed part. As such, enabling additive manufacturing techniques that are informed by the mesostructure would open unprecedented new opportunities, if achieved in a predictive manner using functional materials and scalable processing pathways. The Grand Challenge in developing accurate and reliable additive manufacturing processes for gradient materials is the lack of robust platforms for understandingthe micro, meso, and macroscopic kinematic and structural features that are formed and evolved during the process. Thus, one critically needs to connect structure build-up, rheology and manybody dynamics of such complex systems, and study the resulting multi-scale couplings in their entirety to be able to prescribe additive manufacturing processing conditions for a targeted property portfolio. Variety of potential gradient structures and the intertwining of different processes at play makes their design cycle extremely long and difficult. Here, and by combining a range of advanced computational tools, we will develop neural networks that are informed by the micro-, meso-, and macro-scale. Employing the latest advances in physics-informed AI, we will develop the rheology-informed digital twins of the AM processes with predictive meta-models for sustainable manufacturing of structures with deliberate mesostructural design.Approved for Public Release
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Aug 11, 2023
- Source ID
- N000142312772
Entities
People
- Safa Jamali
Organizations
- Northeastern University
- Office of Naval Research
- United States Navy