ABSTRACT

The objective of this project is to improve the knowledge of materials design by developing a multi-scale methodology that combines physics-based models of thermo-mechanical processing and materials with artificial intelligence (AI) and machine learning (ML). The underlying hypothesis is that the metallic components can be designed to achieve targeted macro-scale properties and performance by optimizing the underlying microstructural features and processing parameters. The project will build design methodology that enables: (i) investigation of the effects of microstructures and processing parameters on macro-scale properties; and (ii) identification of multiple optimum material designs that provide desired macro-scale performance. The ability to optimize macro-scale properties by designing microstructures and processes will improve the performance of current and future engineering systems. Additionally, with the consideration of manufacturing constraints, this multi-scale design framework will not merely identify mathematical solutions, but the designs that will be manufacturable. The researched methods and results will be tested against the experimental data of a Titanium-Aluminum alloy. The societal impacts of the project will be on the economy, with performance improvement in metallic components and minimization of the time and costs associated with manufacturing. The gained knowledge will be disseminated to academia and industry with technical activities and open-access software tools. Additional deliverables of the project include curriculum development at both undergraduate and graduate levels, research and education experiences for students, and other outreach activities involving students and educators with a special focus on individuals from underrepresented groups.

The overarching goal of this project is to advance knowledge in the design of metallic materials by developing a multi-scale optimization strategy that will be driven by the physics-based models of thermo-mechanical processing and microstructures, and AI/ML-based predictive modeling and knowledge discovery approaches. The research will address the inverse design problem that aims to optimize the thermo-mechanical processing parameters (i.e., strain rate, temperature, duration) to achieve desired microstructural features (i.e., crystallographic texture, grain morphology) and macro-scale properties by investigating the coupled, multi-scale, and high-dimensional interactions within the processing-(micro)structure-property chain. To achieve this goal, the project will develop physics-based models that enable explicit quantification of microstructural orientations and morphology (grain sizes and shapes), and an ML-guided feedback-aware identification strategy for key processing/(micro)-structure parameters, which will be subsequently explored by targeted sampling. The research will improve the understanding of inverse materials design by also integrating manufacturing constraints into the design framework and exploring multiple optimum material solutions that provide desired macro-scale properties. The physics-based and AI/ML-driven models, as well as the optimum results obtained by the multi-scale design framework, will be validated using the experimental processing, microstructure, and property data of a Titanium-Aluminum alloy. The education and outreach objectives of the project focus on training students and the future workforce to create new knowledge on computational and ML-driven design of materials, which will be supported with curriculum development and an extensive dissemination and outreach plan.

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.

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