Model-Based Systems Definition and Analysis Integration for Smart Manufacturing

Summary

To develop and standardize methods and protocols that facilitate analysis of products, processes, and logistics, by unifying domain-specific analysis information and integrating it with systems modeling information.

Description

Smart manufacturing system development and operations are difficult to manage because information about the systems and their analysis is expressed in redundant and incompatible ways across the multiple engineering disciplines involved (such as electrical, materials, and process). Integrating information about overall system requirements and structure with discipline-specific information is costly because analysis models use concepts and formats that are partially redundant and inconsistent with each other, and with overall systems models. This project develops methods and protocols to enable efficient integration of smart manufacturing systems models and analysis models, by unifying disciplinespecific analysis information and integrating it with existing systems modeling information. Manufacturers
and solution providers will be able to operate smart manufacturing systems faster and cheaper, by analyzing systems more efficiently. NIST has extensive and unique experience in enabling efficient exchange and use of systems and engineering information between tools. It is best positioned as a neutral party to make this available to manufacturers and solution providers developing and operating smart manufacturing systems.

WHAT IS THE TECHNICAL IDEA?
Smart manufacturing system development and operations are difficult to manage because information about the systems and their analysis is expressed in redundant and incompatible ways across the multiple engineering disciplines involved. Redundant and inconsistent information exists between:

1. Systems and analysis models: discipline-specific analysis describes some system components and their interactions in detail, but not all, while overall system models describe all components and interactions in less detail.
2. Analysis models on multiple tools: each kind of discipline-specific analysis model is supported by multiple, incompatible tools, which encode the same analysis information, usually inconsistently.

For example, many disciplines use tools that simulate physical interaction and signal flow between system components to predict whether system specifications and operation plans will meet requirements. These tools cover portions of overall systems information, but redundantly and inconsistently with each other and with systems models.

The technical idea is to use overall system models to coordinate discipline-specific engineering analysis by identifying and eliminating inconsistencies between systems models and analysis models. The project will organize analysis information around overall system models to reduce redundancy and inconsistency, by developing standard methods and protocols that prevent redundancies and inconsistencies between 1) systems and analysis models, enabling manufacturers to coordinate multiple analyses of the same system and 2) analysis models on multiple tools, enabling manufacturers to use analysis tools most suited to their problems.

WHAT IS THE RESEARCH PLAN?
Systems models capture customer requirements, high-level system specifications, verification tests, and the relationships between them. They gather overall systems information in one format, improving consistency and reducing redundancy. The project will use overall system models to coordinate discipline-specific engineering analysis by identifying and eliminating inconsistencies between systems and analysis models, and between analysis models themselves. The high-level plan is:

1. Identify discipline-specific analysis methods and tools useful in manufacturing development operations. Focusing the project on these analysis methods and tools will provide the greatest impact for integration. The selected analysis methods currently are discrete event logistics/production simulation and optimization, finite element analysis, and behavior verification.
2. Identify redundancies and inconsistencies between systems models and the selected analysis methods and tools.
3. Develop methods and protocols that prevent redundancies and inconsistencies between systems and analysis information, either by maintaining links between systems and analysis information, or by consolidating analysis information in overall systems models.
4. Develop methods and protocols that prevent redundancies and inconsistencies between analysis information on multiple tools, by
   a.    identifying and abstracting commonalities among specific kinds of discipline-specific analysis information that are useful in smart manufacturing operations
   b.    maintaining links between information on multiple analysis tools, or by consolidating it in extensions of overall systems models, based on the abstractions above.

The research plan will be carried out with logical formalizations of system and analysis information to identify inconsistencies, and guide development of methods and protocols to eliminate them. The result will enable systems modeling tools and discipline-specific analysis tools to efficiently exchange and use information during smart manufacturing development operations.