The development and manufacturing of cutting edge materials typically involves time-consuming materials and process design phases, followed by extensive testing of samples to adjust process conditions as the manufacturing scales up from the lab, to pilot plan, to industrial process scale. These distinct steps drive up the cost barrier to introduction of new and improved materials into the industrial pipeline and increase the cost of domestically manufactured advanced nanomaterials. Fortunately, recent developments in numerical modeling, additive manufacturing, and rapid testing of materials suggest that a new approach to material development and nanomanufacturing, where the previously distinct and time-consuming phases could be carried out nearly instantaneously to arrive at optimal material structure as well as process conditions for its manufacture. The focus of this award is to revamp the traditional, open-loop synthesis of nanostructured materials by: 1) using a versatile 3-D printing approach to manufacture these nanomaterials and nanostructures, 2) incorporate material property characterization directly into the printing process, and 3) use an artificial intelligence (AI) algorithm to adjust on the fly process conditions to achieve desired material properties. These concepts and components will be integrated into technical coursework, hands-on research opportunities, and outreach workshops to a broad range of students and the public. The co-PIs plan to leverage existing outreach and educational activities through their group's collaboration with a local museum, as well as curricular and extracurricular activities.
The approach and framework is an investigation of process modeling, materials synthesis and characterization, and system design to autonomously discover new material configurations and reduce manufacturing defects and uncertainty. This AI framework will "understand" process-structure-property relationships, manufacturing constraints, and, importantly, statistical variations in material properties and manufacturing quality. The intellectual merit of this study is the discovery of general nanomanufacturing tools and feedstocks, with supervisory genetic algorithms, that autonomously correct for defects and compensate for innate manufacturing inaccuracies by a search for alternative designs; this is in contrast to standard tools that minimize uncertainty (e.g. environmental controls) or rely on post-fabrication characterization with human intervention. The framework will be tested using nanoscale additive manufacturing (AM) as the fundamental manufacturing tool and nanostructured metamaterials as the application. The paradigm and nanoscale metamaterials made via this approach have far-reaching impacts on scalable nanomanufacturing for integrated systems. The paradigm of systems that autonomously evolve parameters to meet construct specifications is extensible to macroscale additive manufacturing and pharmaceuticals where the process parameter space and chemistries available is vast, and design is not intuitive. Additive nanomanufacturing has the potential to transform metamaterial design by enabling design in 3-dimensions (3D) with multiple materials, creating complex composite metastructures.
