Product design is a highly involved, often ill-defined, complex and iterative process, and the needs and specifications of the required artifact get more refined only as the design process moves toward its goal. An effective computer support tool that helps the designer make better-informed decisions requires efficient knowledge representation schemes. In today's world, there is a virtual explosion in the amount of raw data available to the designer, and knowledge representation is critical in order to sift through this data and make sense of it. In addition, the need to stay competitive has shrunk product development time through the use of simultaneous and collaborative design processes, which depend on effective transfer of knowledge between teams. Finally, the awareness that decisions made early in the design process have a higher impact in terms of energy, cost, and sustainability, has resulted in the need to project knowledge typically required in the later stages of design to the earlier stages. Research in design rationale systems, product families, systems engineering, and ontology engineering has sought to capture knowledge from earlier product design decisions, from the breakdown of product functions and associated physical features, and from customer requirements and feedback reports. VR (Virtual reality) systems and multidisciplinary modeling have enabled the simulation of scenarios in the manufacture, assembly, and use of the product. This has helped capture vital knowledge from these stages of the product life and use it in design validation and testing. While there have been considerable and significant developments in knowledge capture and representation in product design, it is useful to sometimes review our position in the area, study the evolution of research in product design, and from past and current trends, try and foresee future developments. The goal of this paper is thus to review both our understanding of the field and the support tools that exist for the purpose, and identify the trends and possible directions research can evolve in the future.

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The evolution, challenges, and future of knowledge representation in product design systems

Designing materials for targeted performance requirements as required in Integrated Computational Materials Engineering (ICME) demands a combined strategy of bottom-up and top-down modeling and simulation which treats various levels of hierarchical material structure as a mathematical representation, with infusion of systems engineering and informatics to deal with differing model degrees of freedom and uncertainty. Moreover, with time, the classical materials selection approach is becoming generalized to address concurrent design of microstructure or mesostructure to satisfy product-level performance requirements. Computational materials science and multiscale mechanics models play key roles in evaluating performance metrics necessary to support materials design. The interplay of systems-based design of materials with multiscale modeling methodologies is at the core of materials design. In high performance alloys and composite materials, maximum performance is often achieved within a relatively narrow window of process path and resulting microstructures. Much of the attention to ICME in the materials community has focused on the role of generating and representing data, including methods for characterization and digital representation of microstructure, as well as databases and model integration. On the other hand, the computational mechanics of materials and multidisciplinary design optimization communities are grappling with many fundamental issues related to stochasticity of processes and uncertainty of data, models, and multiscale modeling chains in decision-based design. This paper explores computational and information aspects of design of materials with hierarchical microstructures and identifies key underdeveloped elements essential to supporting ICME. One of the messages of this overview paper is that ICME is not simply an assemblage of existing tools, for such tools do not have natural interfaces to material structure nor are they framed in a way that quantifies sources of uncertainty and manages uncertainty in representing physical phenomena to support decision-based design.

Key computational modeling issues in Integrated Computational Materials Engineering

Design verification and validation in product lifecycle

As systems continue to grow in scale and complexity, the Systems Engineering community has turned to Model‐Based Systems Engineering (MBSE) to manage complexity, maintain consistency, and assure traceability during system development. It is different from “engineering with models,” which has been a common practice in the engineering profession for decades. MBSE is a holistic, systems engineering approach centered on the evolving system model, which serves as the “sole source of truth” about the system. It comprises system specification, design, validation, and configuration management. Even though MBSE is beginning to see a fair amount of use in multiple industries, specific advances are needed on multiple fronts to realize its full benefits. This paper discusses the motivation for MBSE, and its current state of maturity. It presents systems modeling methodologies and the role of ontologies and metamodels in MBSE. It presents model‐based verification and validation (V&V) as an example of MBSE use. An illustrative example of the use of MBSE for design synthesis is presented to demonstrate an important MBSE capability. The paper concludes with a discussion of challenges to widescale adoption and offers promising research directions to fully realize the potential benefits of MBSE.

Model-based systems engineering: Motivation, current status, and research opportunities

Reliability study of complex physical systems using SysML

OMG SysML™ is a modeling language for specifying, analyzing, designing, and verifying complex systems. It is a general-purpose graphical modeling language with computer-sensible semantics. This Part 1 paper and its Part 2 companion show how SysML supports simulation-based design (SBD) via tutorial-like examples. Our target audience is end users wanting to learn about SysML parametrics in general and its applications to engineering design and analysis in particular. We include background on the development of SysML parametrics that may also be useful for other stakeholders (e.g, vendors and researchers). In Part 1 we walk through models of simple objects that progressively introduce SysML parametrics concepts. To enhance understanding by comparison and contrast, we present corresponding models based on composable objects (COBs). The COB knowledge representation has provided a conceptual foundation for SysML parametrics, including executability and validation. We end with sample analysis building blocks (ABBs) from mechanics of materials showing how SysML captures engineering knowledge in a reusable form. Part 2 employs these ABBs in a high diversity mechanical example that integrates computer-aided design and engineering analysis (CAD/CAE). The object and constraint graph concepts embodied in SysML parametrics and COBs provide modular analysis capabilities based on multi-directional constraints. These concepts and capabilities provide a semantically rich way to organize and reuse the complex relations and properties that characterize SBD models. Representing relations as non-causal constraints, which generally accept any valid combination of inputs and outputs, enhances modeling flexibility and expressiveness. We envision SysML becoming a unifying representation of domain-specific engineering analysis models that include fine-grain associativity with other domain- and system-level models, ultimately providing fundamental capabilities for next-generation systems lifecycle management.

http://www.eislab.gatech.edu/pubs/conferences/2007-incose-is-1-peak-primer/2007-incose-is-1-peak-primer.pdf

Simulation-Based Design Using SysML Part 1: A Parametrics Primer

One important aspect of product lifecycle management (PLM) is the computer-sensible representation of product information. Over the past fifteen years or so, several languages and technologies have emerged that vary in their emphasis and applicability for such usage. ISO 10303, informally known as the Standard for the Exchange of Product Model Data (STEP), contains the high-quality product information models needed for electronic business solutions. By using STEP, the aerospace, automotive, and ship building industries are saving $150M per year primarily in areas related to geometric modeling. However, traditional STEP-based model information is represented using languages that are unfamiliar to most application developers, thus impeding widespread usage in other areas. This paper discusses efforts underway to make STEP information models available via mechanisms familiar to more business application developers, specifically XML and the Unified Modeling Language™ (UML®). We also present a vision and roadmap for STEP integration with XML, UML, and other technologies to enable enhanced PLM interoperability. Our conclusion is that STEP, XML, and UML are complementary technologies, where STEP provides significant standardized content models, while XML and UML provide enhanced implementation methods. Together, they are a powerful force to enable pervasive digital representation and sharing of diverse technical information.

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STEP, XML, and UML: Complementary Technologies

With the present gap between CAD and CAE, designers are often hindere in their efforts to explore design alternatives and ensure product robustness. This paper describes the multi-representation architecture (MRA)—a design-analysis integration strategy that views CAD-CAE integration as an information-intensive mapping between design models and analysis models. The MRA divides this mapping into subproblems using four information representations: solution method models (SMMs), analysis building blocks (ABBs), product models (PMs), and product model-based analysis models (PBAMs). A key distinction is the explicit representation of design-analysis associativity as PM-ABB idealization linkages that are contained in PBAMs. The MRA achieves flexibility by supporting different solution tools and design tools, and by accommodating analysis models of diverse discipline, complexity and solution method. Object and constraint graph techniques provide modularity and rich semantics. Priority has been given to the class of problems termedroutine analysis—the regular use of established analysis models in product design. Representative solder joint fatigue case studies demonstrate that the MRA enables highly automated routine analysis for mixed formula-based and finite element-based models. Accordingly, one can employ the MRA and associated methodology to create specialized CAE tools that utilize both design information and general purpose solution tools.

http://www.eislab.gatech.edu/pubs/journals/ewc-mra/ewc-mra.pdf

Integrating Engineering Design and Analysis Using a Multi-Representation Approach †

These two companion papers present foundational principles of parametrics in OMG SysML™ and their application to simulation-based design. Parametrics capabilities have been included in SysML to support integrating engineering analysis with system requirements, behavior, and structure models. This Part 2 paper walks through SysML models for a benchmark tutorial on analysis templates utilizing an airframe system component called a flap linkage. This example highlights how engineering analysis models, such as stress models, are captured in SysML, and then executed by external tools including math solvers and finite element analysis solvers.



We summarize the multi-representation architecture (MRA) method and how its simulation knowledge patterns support computing environments having a diversity of analysis fidelities, physical behaviors, solution methods, and CAD/CAE tools. SysML and composable object (COB) techniques described in Part 1 together provide the MRA with graphical modeling languages, executable parametrics, and reusable, modular, multi-directional capabilities.



We also demonstrate additional SysML modeling concepts, including packages, building block libraries, and requirements-verification-simulation interrelationships. Results indicate that SysML offers significant promise as a unifying language for a variety of models—from top-level system models to discipline-specific leaf-level models.

http://www.eislab.gatech.edu/pubs/conferences/2007-incose-is-2-peak-diversity/2007-incose-is-2-peak-diversity.pdf

9.3.3 Simulation‐Based Design Using SysML Part 2: Celebrating Diversity by Example

Computer simulations and behavioral modeling are becoming increasingly important in product development processes. Simulations can result in better decisions in less time by providing the designers with greater understanding of the product’s behavior. However, behavior model creators (i.e. analysts) and behavior model users (i.e. designers) often do not have the same level of understanding of the model, thus limiting the reuse of a model. Our goal in this research is to develop a clean interface that reduces the knowledge gap between engineering design and analysis by facilitating reuse of behavioral models. To achieve a higher level of reuse in the product design process, we propose a meta-data representation for formally characterizing behavioral models. The meta-data representation captures the assumptions, limitations, accuracy, and context of engineering behavioral models. Based on this knowledge representation, a proof-of-concept repository is implemented for archiving and exchanging reusable behavioral models. The knowledge representation and implementation is illustrated with a simple cantilever beam example.

Russell S. Peak

Papers

Simulation-Based Design Using SysML Part 1: A Parametrics Primer

STEP, XML, and UML: Complementary Technologies

Integrating Engineering Design and Analysis Using a Multi-Representation Approach †

9.3.3 Simulation‐Based Design Using SysML Part 2: Celebrating Diversity by Example

A knowledge repository for behavioral models in engineering design