ICME Platform Foundational Capabilities - Sreedhar Reddy

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Brice Oliver
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1 Questions to be addressed What are the foundational principles for platformization of approaches for the integrated realization of engineered materials and products? What information models can be flexible in the development of ontologies and interoperability of disparate systems? Specifically for applications in evolution of design and manufacturing information and design processes? How can the flow of information through the chain of simulations be managed both effectively and accurately? Computational Tools: What is the best approach to provide scalable and maintainable computational infrastructures for ICME?

2 ICME Platform Foundational Capabilities - Sreedhar Reddy

3 What is a platform? A platform provides the enabling technology base for effective solutions in a problem domain What is the problem domain? Industrialization of computational modeling approaches for Integrated realization of engineered materials and products

4 A few foundational principles for an effective platform Avoid coding where modelling will do Models are easier to understand, analyze and change Leading to a more flexible system architecture Critical since the concept of ICME is still evolving Access to high quality, easily useable, purpose specific knowledge is the key to industrialization of ICME Knowledge of product design, materials, processes, models, simulation A built-in continuous learning capability is the key to steadily improving problem solving capacity Learning from what worked and what did not

5 Model Driven Architecture

6 Model driven architecture core infrastructure Meta meta model A language to define meta models Meta model A language to define models Model Meta modeling capability A flexible means to define purpose specific modeling languages Model transformations capability A means to transform one model into another model, or a model into code

7 A sample meta model relational data model Attribute Class Association Meta meta model Column datatype * 1 Table Meta model Person Name: varchar Address: varchar Model 7

8 Meta modeling for flexible ontology engineering Component meta model Subject Area Meta Model Component 1 1..* Feature instanceof instanceof Subject Area Models Gear model Gear:Component Teeth:Feature Ontologies are central to the platform Serve as a means for interoperability and information exchange Serve as a means for capturing data and knowledge Meta modeling of ontologies ontologies as first class entities Key to flexibility and extensibility of the platform

9 Materials Ontology

Model-driven architecture at play examples Data virtualization Flexibility to change underling data storage mechanisms without impacting consuming components

10 Model-driven architecture at play examples Data virtualization Flexibility to change underling data storage mechanisms without impacting consuming components Relational, in-memory, distributed, BigData, etc Data access Conceptual Model/Ontology Model Mapping Model compiler Data access layer Physical Data Model Database

11 Model-driven architecture at play examples Model-driven process execution Process work-flow models Execution strategy derived from info captured in models Scheduling of simulation tools Data transformations UserTask KnowledgeTask SimulationTask GUI Model KnowledgeService SimulationService Knowledge Model Simulation Model

12 Model-driven architecture at play examples Process integration through global state previous Product State Component Model Material State next input output Proc X Proc Y Proc Z Simulation process chain

13 Model-driven architecture at play examples Tool integration Generate tool wrappers from model mappings Global state ontology Mapping Mapping Tool specific data view Tool specific data view input output input output Simulation tool 1 Simulation tool 1

14 Model-driven architecture at play examples Data integration Domain Ontology mapping Context specific Data model Context specific Data model Context specific Data model Wrapper Wrapper Wrapper Structured data Semi structured data Unstructured data

15 Knowledge Engineering

16 Knowledge modeling Materials engineering is a knowledge intensive activity Knowledge of composition process structure property relations Modeling knowledge what models and governing equations should be selected for a given phenomena in a given problem context Simulation knowledge selecting right simulation control parameters, dimensionality reduction (3-D to 2-D to 1-D), selecting right mesh section, setting up right boundary conditions, etc Capturing and providing this knowledge in a task-specific, context sensitive manner is the key to scaling up usage of ICME How do we achieve this? To start with, we need a clear conceptual map of how different problem contexts should be modeled, and how knowledge of different kinds should be represented

17 Knowledge modeling Domain ontology subclassof subclassof subclassof subclassof Component Material Process Phenomenon Context Task Topic subclassof Publication Procedure uses KnowledgeElement Source Experiment Rules DecisionTree Model Cases Template ANN SVR Probabilistic Graphical model

18 Knowledge mining Text mining Mining data and knowledge from publications, on-line sources Data mining Mining experimental data Mining decision traces left behind on the platform Learning generalizations Knowledge is captured in a specific context It needs to be generalized to be applicable in other similar contexts E.g. To simulate heat transfer in casting, it is sufficient to take the upper right quarter of a 2D section of the three dimensional cast Underlying reason: component (i.e. slab) is symmetric, the two sides are free, there are no external forces This generalizes to any situation that is similar, not just to casting

19 Thank you

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