Materials property data, and broader materials information, are essential to the wide range of people and functions in engineering enterprises involved in the design and construction of power generation facilities. This paper focuses on how this complex and specialist information must be managed, and how it can be deployed most effectively to those who need it. There are many different types of data to be considered, but they all begin as test results of one form or another. Generating this test data represents a major cost for many organizations. Applying it effectively can give them a major competitive advantage — for example, avoiding problems in design, gaining more understanding of the performance of materials, and ultimately reducing maintenance costs or enabling asset life extension. Yet few organizations have in place any systematic system to manage materials data. Not only does this mean that they are not making best use of their investment in this asset, it means that they can actually waste large amounts of time and money, in searching for the right data, or in duplicating tests that have already been done. This paper concentrates on the practical challenges involved, and on the technical details of how these challenges can be solved, drawing on the experience of developing the GRANTA MI materials information management system in collaboration with a consortium of leading aerospace, energy, and defense organizations. Three use-case scenarios are explored in which the management and application of materials data are important. These are: support for engineering design, statistical process control, and materials selection. For each use-case, the technical requirements for any corporate materials data management system are identified. Significant overlaps are found between the requirements for the different areas, indicating that a well-designed system can both meet a broad range of specific needs, and help to integrate different aspects of an organization’s operations. It is then appropriate to discuss the actual software tools required to meet these needs. These include tools to capture and consolidate materials data, to analyze and apply the data, to maintain a corporate materials information resource, and to deploy materials data enterprise-wide to the different functions (e.g., Design, and Quality Assurance) and roles (e.g., design engineers, stress analysts, and process improvement managers) that require it. The paper closes by providing guidelines on best practice regarding implementation of materials data management technology.
Understanding the Gap Between Information Models and Realism-Based Ontologies Using the Generic Component Model