| Summary: | The Materials Genome Initiative was announced by the White House in June of 2011, and is a multi-agency initiative which calls the materials community to find ways to discover, develop, manufacture, and deploy advanced materials systems faster and more cost-efficiently. Currently, the amount of time it takes to discover and develop a new material system, optimize its properties, integrate it in to a system, certify that system, and develop the manufacturing capability so that it can be deployed in a commercial component takes at least 20 years. Since this trend holds regardless of the material system in question, the implication is that it is the process by which we as a community move through these seven steps, which causes the lengthy timeline. Historically, the discovery, development, and property optimization of a material system relies heavily on deep scientific knowledge, intuition and trial-and-error physical experimentation. Therefore much of the design and testing of materials in these early stages is currently performed through time-consuming and repetitive experimental and characterization feedback loops. Some of these feedback loops could be eliminated in the property optimization step with improved powerful and accurate computational modeling tools. However, while the ability of computational models to be used in this way is not new, models that have been developed in this space have consistently underperformed. Oftentimes, these models fail because they fail to accurately account for the various physical and chemical mechanisms that are driving the system, or because they fail to account for all of the variables which must be included. Here we propose a standard method of communication for these relationships in the form a process-structure-property-performance map, which leverages the known knowledge database of the material system to clearly and visually communicate the relevant variables and their various relationships in a defined materials design space. Such a map is developed here for high-strength Al-Zn-Mg-Cu alloys, which offer a good example of a material system which could benefit from such a standard. This class of alloys, which are typically utilized in aircraft components, have been incorporated in commercial components for nearly 75 years, and due to its long history is a well characterized and well developed system that is highly suited to this kind of examination. In Part I of this work, we develop this standard by first examining the known knowledge database in this system to deduce what the important process, microstructure, and mechanical property variables are that are of interest. Once these variables and the relationships between them are identified, they are organized into a PSPP map according to a proposed set of steps, and can act as a visual standard that can clearly communicate critical information about the mechanisms of the system. For example, if a model developed within this system does not include a variable or a mechanism depicted within the map, it can be used to communicate the ways in which the model will be constrained. Similarly, when experimental data is collected within this space the map can be used to clearly communicate which variables in the space were held constant, which variables were tracked and accurately measured, and if any variables were unaccounted for. This information can help to communicate what situations the data can be used in, and how the space that the experimental data can be used in is constrained. In Part II of this work, we vary multiple parameters within the high-strength Al-Zn-Mg-Cu system defined in Part I, and attempted to track and measure as many of the variables within the space as possible using commonly available testing and characterization methods. In tackling such a large project in the complicated materials system of high-strength wrought Al-Zn-Mg-Cu alloys, we are able to understand which current testing and characterization methods are well suited to tracking these variables when the number of test specimens becomes quite large and when variability among those specimens is involved. We are also able to identify opportunities for future work in this area, which could be focused on improving our ability to implement projects of the scope that is required here. In addition to evaluating the feasibility of the various measurement and characterization methods, the raw data and the analyzed results for this work are cataloged in an associated data repository and have been made available for use in future work in this and other areas.
|
|---|
