NSF Award
2118769
Materials manufacturing is subject to uncertainty in raw materials and processes that can drastically alter the resulting properties. Defects can lead to costly re-tuning of the process recipes and prevent production of certified products. To address these challenges, this Designing Materials to Revolutionize and Engineer our Future (DMREF) award will support the development of a data-centric approach for integrated materials design and manufacturing, called Materials Architected by Adaptive Processing (MAAP). In this approach, the same data and data infrastructure applied for materials design will be harnessed for process monitoring and control to ensure consistent production of materials with targeted properties. As a specific example, the MAAP approach will be used to design and produce polymer blends with superior properties from recycled material. The award will also advance fundamental understanding of mixing and flow-induced crystallization in multi-polymer melts and apply that understanding to produce architected blends from recycled polymers. The ability to upcycle plastic waste will improve sustainability and reduce the environmental impact of plastics production. The project will contribute to the workforce development by: 1) developing student projects that integrate data science, experimentation, and computation; 2) providing research internships for high-school students traditionally underrepresented in engineering; and 3) offering training for practicing engineers, technicians, and managers in recycling, melt blending, and quality control.<br/><br/>The goal of this project is to develop the integrative MAAP approach to make the materials-by-design process more robust and provide adaptive processing systems for consistent materials production. This goal will be pursued in the context of superior polymer blends obtained from recycled polyethylene and isotactic polypropylene. The approach involves five key interrelated tasks. An event-driven, microservices data layer will automate the contextualized data flow between the processing, characterization, multiscale modeling, decision, and control tasks of the project. An instrumented co-extrusion process with modular shape-multiplying elements will be designed based on the modeling studies to investigate the controllability of the melt streams and observability of the architected blends. Micro-scale modeling of flow-induced crystallization (FIC) of the multi-polymer melt system will study the effects of the domain interfaces and processing conditions on the development of crystalline morphologies and provide material models of FIC for meso-scale studies. Meso-scale models will investigate the stability of the melt streams through the shape-multiplying elements and the formation of phase domains during processing to determine how the measurable processing parameters control the crystalline morphologies and domain architecture. Materials characterization and micromechanical modeling will investigate the effect of the crystalline and domain structures on the mechanical properties of the architected blends.<br/><br/>This project is co-funded by the Division of Civil, Mechanical and Manufacturing Innovation in the Directorate for Engineering and the Division of Materials Research in the Directorate for Mathematical and Physical Sciences.<br/><br/>This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
