QRM: Using Visual Information to Quantify Microstructure-Processing-Property Relationships

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To advance technology for infrastructure and manufacturing, new, high-performance materials must be developed, with tunable properties (for example, strength or electrical conductivity) and predictable performance. The computational tools used to predict material performance have often relied on Materials Engineers examining pictures of a material's microscopic substructure, termed the microstructure, to determine the relationships between microstructural features and material properties. The analysis of microstructural images can be expensive and time consuming, and can produce inconsistent results. This award supports fundamental research to build the tools to enable an automated approach to microstructure image analysis, with the aim of better understanding the microstructural features that control material properties and performance. In this project, researchers will collect a large set of microstructural images and use artificial intelligence (AI) tools including computer vision and machine learning to analyze them. The AI system will autonomously identify different features of the microstructure, measure them, and link them to how the materials was made (processing) and how it performs (properties). The advantages of this system are that it is fast, objective, general, and may perceive information that is not readily visible to humans. To test and validate this approach, it will be applied to understanding scientifically challenging problems with application in advanced manufacturing, including predicting the strength of materials, and determining the limits of microstructural information. This project will additional train Materials Science and Engineering students in the principles of AI. Furthermore, the methods and results of this project will be made publicly available.The quantitative representation of microstructure is the foundational tool of microstructural science and traditionally involves a human deciding a priori what to measure and how to measure it. However, recent advances in data science, including computer vision (CV) and machine learning (ML) offer new approaches to extracting information from microstructural images. In this project, the PIs will acquire, curate, and publish a diverse collection of microstructural image data sets, including composition, processing, and property metadata, and will build a suite of CV/ML tools for autonomous microstructural image representation and quantification. The CV/ML approach will be applied to finding quantitative composition-microstructure-processing-property relationships, with the goal of materials discovery. Case studies will focus on achieving scientific understanding of additive manufacturing processes; enhancing knowledge of deformation mechanisms; advancing microstructural science to include visual signals that are not perceptible by humans; and assessing the degree of scientific knowledge learned by a black-box ML method. The methods and results developed in this project will be disseminated via open access codes and data repositories, and will contribute to workforce development by educating Materials Science and Engineering students in the principles of computer vision and machine learning.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.