Recently, there has been a significant renewed interest exploiting the advantages afforded by additive manufacturing. Often, the impetus for such efforts have been manufacturing objectives, such as the ability to produce net shapes, repair components, or reduce the cost or time for either product development or product deployment. This has created a technically misguided situation where the development of the manufacturing technique has proceeded absent a concurrent co-development of materials. No doubt, this situation exists in part due to the asymmetry in the rates of technology development of manufacturing techniques (fast) and new materials (slow). Yet, there is a tremendous opportunity to design new materials for additive manufacturing that exhibit structures and attending properties that are otherwise not achievable via traditional manufacturing approaches. Indeed, additive manufacturing of metallic structures involves moving point sources of energy that are sufficiently large so as to result in localized melting followed by rapid (or near-rapid) solidification, and consequently far-from equilibrium materials. Such materials are characterized by improvements in both structure and properties, but have historically been avoided as they were achievable in limited product forms with constrained dimensions. Additive manufacturing has the potential to produce structures similar to those of rapid solidification processing (rsp), but in a bulk, net-shape product form. Clearly, if the current focus on manufacturing remains without a strategy of concurrent development/design of materials for additive manufacturing, the full potential of additive manufacturing will not be realized.
As proposed, this DMREF project would integrate experts in high-throughput combinatorial materials science, state-of-the-art materials characterization, computational materials science, and data-science to: (1) discover and model the fundamental interrelationship between the alloy composition, the far-from equilibrium conditions of AM processes, and the resulting microstructure and properties; (2) describe rigorously the materials composition and structure using multi-dimensional, multi-spatial, and multi-spectral approaches; (3) determine, via powerful data science approaches, hidden correlations (e.g., mechanisms) between composition, structure, and properties as assessed using both computational and experimental techniques; (4) validate the mechanisms via computational modeling and critical experimentation; and (5) design, a new material for additive manufacturing processes. The framework would be disseminated not only among universities, but also directly to industrial partners via both an existing NSF-I/UCRC and a “Industrial Dissemination Workshop” to be conducted at the conclusion of the program.
This program is well-aligned with the Materials Genome Initiative (MGI) and the corresponding NSF program, Designing Materials to Revolutionize and Engineer our Future. It seeks to augment the very real promise of additive manufacturing, providing a materials design framework that can be adopted for various materials systems and engineered products to increase the global competitiveness of American companies pursuing AM. The team is very qualified, having been associated with Integrated Computational Materials Engineering (ICME), a framework that predates but parallels the MGI.