Founded in 1994 as a spin-out from the University of Cambridge, Granta helps hundreds of engineering enterprises to manage information on the materials (metals, plastics, composites, and more) that are essential to their businesses. We help them to develop and apply material intelligence, making better materials decisions, saving time and money, and reducing risk as they optimize their products. We also provide supporting resources to thousands of university educators worldwide as they teach the next generation of engineers, scientists, and industrial designers about materials, processes, and sustainability.
Collaboration is central to our approach—with customers, with industry consortia and international collaborative research projects such as AMAZE, with the global materials education community, and with other providers of engineering software, materials data, and materials services.
The AMAZE project generates a large volume of engineering, processing, economic, and environmental data relating to the materials used in additive manufacturing with metals. It is essential to make the best possible use of this data in order to improve understanding of the AM process and to then share and apply this newly-developed knowledge.
Granta is providing an overarching materials information management system for the consortium, enabling partners to pool materials data and to create a single go-to source of knowledge on materials, processes, and properties for process refinement and coordination. The database captures all the key project data from materials procurement, manufacturing, inspection and testing.
This materials information management system is based on GRANTA MI, the world’s leading materials information management system for engineering enterprises. GRANTA MI has specialist data structures for managing the complexities of materials data. It is possible to configure these structures (the so-called ‘schema’) to the specific requirements of the user organization or project. Within AMAZE, the Granta team worked closely with the project partners to develop and implement a detailed schema that can manage the different types of data of interest for additive manufacturing—for example, on material properties of particular interest, equipment and process parameters, test data, simulation, and qualification of parts.