CAREER: A Systematic Data-Analytics Approach to the Design of Interface-Rich Materials

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Interface-rich materials are pervasive in all engineered systems, including turbine discs for energy harvesting, and bonded layered composites in medical imaging equipment. The mechanisms that control performance of interface-rich materials manifest at the mesoscale, a length scale between the nanometer and macroscopic system (millimeter to meters) scales. Therefore, efficient design of these materials to achieve the performance metrics necessary for an engineered system (i.e., thermal resistance for x-ray generation) requires a quantitative understanding of the manufacturing processes and their relationship to the mechanisms at this "in-between" length scale. This Faculty Early Career Development (CAREER) award supports fundamental research needed integrate the theory of mesostructure performance, and statistical-based analysis of "big data." The design paradigm will be applied specifically to a forged nickel-based superalloy, and has the potential for broad impact on the rapid insertion of new materials and manufacturing processes to reduce cost and time-to-market for engineered systems which interface-rich materials.The research goal is to test the hypothesis that statistical data analytics can lead to a versatile design paradigm for the manufacturing of interface-rich materials for particular performance requirements. The predictive capabilities of these data-derived models will be assessed for Inconel-706 a forged Ni-based superalloy, with the performance metrics of high temperature strength and low cycle fatigue life. This alloy is significant for the manufacturing of efficient energy harvesting applications. The data for this research includes manufacturing, mesostructure and performance measures from legacy data obtained over the past 10 years by Alcoa Forging Research Group. The research team will test this hypothesis by cultivating an open, robust, data infrastructure that goes beyond a simple electronic "filing cabinet" and allows for seamless access to the data by analysis tools and allows for the analysis results to also be stored with all the associated metadata. Exploratory data analysis will guide the team in determining what additional datasets are needed to increase the statistical validity of the data-derived models. This research program supports broader efforts of the Material Science community through both the Materials Genome Initiative and Integrated Computational Materials Engineering by producing a robust, open-source data framework, which is generalizable to manufacturing routes of other interface-rich materials.