ASME International Mechanical Engineering Congress & Exhibition (IMECE)

Montreal, Canada

Nov 14-20, 2014

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Granta is speaking at this event.

Granta help engineering organizations to manage, analyze, and apply critical data on materials and processes. We collaborate closely with these enterprises to develop tools and information resources that overcome the particular challenges of working with advanced materials. Find out more when you hear Granta speak on "Factors affecting systems for managing materials information, modeling predations and experimental results".

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Granta Presentation

Granta Speaker: Dr Will Marsden

Tuesday, November 18 - 9:45-11:30am
Track 11 Materials: Genetics to Structures, Topic: 11-19-1 Advancements in methods, tools and experimental techniques towards ICME

Factors affecting systems for managing materials information, modeling predations and experimental results.

Working with the Materials Data Management Consortium (MDMC) and ASM’s National Center for Computational Materials Science and Engineering Data has shown how the right software tools are needed to maintain, visualize, and analyze the data, and to fully integrate it with computer aided design and analysis packages. Underlying this, and crucial to providing secure, traceable, and accurate integration, is an information architecture with the flexibility to store and utilize the full range of materials information. At the heart of an Integrated Computational Materials Engineering (ICME) system must be structures—flexible database schema—to handle the complexity of materials data. Experimental details—including machine-types, processing parameters, temperatures, feeds and speeds, dates, names, etc.—must be maintained together with the experimental results. Computer modeling and simulation of the same situation can have just as many parameters, yet will require different structures to handle this data. Here, the metadata may relate to the model, algorithm, or analysis version. It will be linked to model verification and validation and will refer back to the input deck and pedigree of any input data. This paper will emphasize how, in both cases, the nature of these results will be different across the length scales, and yet the same structures must maintain all this information: microstructure, for example, must be stored alongside predicted properties of entire components. Regardless of whether the data is experimental or virtual, atomistic or macroscopic, the overarching principles remain the same: provided the correct architecture is in place, powerful and flexible tools can be built upon it. These tools will ensure the data is secure, traceable, and reliable, giving clear indications of data quality. The output from one analysis will be used as input to another: all these results (and metadata) will feed back into the knowledge base at the heart of such a system. This paper will report on how these systems perform in real life, drawing on the work of the MDMC. Best practice materials data management can be the foundation of successful ICME. Such approaches draw together materials expertise, ensure data consistency, and minimize the need to recollect data. This best practice is already being implemented in secure implementations within individual corporations where it is reducing risk, saving time, and driving better design.

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