Discovering the Real Problematic Situation: The First Aspect of Conceptual Design

Ring, Jack

doi:10.1002/inst.201417411

Cited by 3 publications

(3 citation statements)

References 3 publications

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“…The objectives can be qualitative (i.e., maximizing or minimizing). The propagation of the objectives in the causal graph may generate contradictions (Ring, 2014). For example, the resulting objective of the propagation can lead to variables that should simultaneously answer contradictory objectives (Warfield, 2002).…”

Section: The Dacm Frameworkmentioning

confidence: 99%

Function modeling combined with physics-based reasoning for assessing design options and supporting innovative ideation

Mokhtarian

Coatanéa

Paris

2017

AIEDAM

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Functional modeling is an analytical approach to design problems that is widely taught in certain academic communities but not often used by practitioners. This approach can be applied in multiple ways to formalize the understanding of the systems, to support the synthesis of the design in the development of a new product, or to support the analysis and improvement of existing systems incrementally. The type of usage depends on the objectives that are targeted. The objectives can be categorized into two key groups: discovering a totally new solution, or improving an existing one. This article proposes to use the functional modeling approach to achieve three goals: to support the representation of physics-based reasoning, to use this physics-based reasoning to assess design options, and finally to support innovative ideation. The exemplification of the function-based approach is presented via a case study of a glue gun proposed for this Special Issue. A reverse engineering approach is applied, and the authors seek an incremental improvement of the solution. As the physics-based reasoning model presented in this article is heavily dependent on the quality of the functional model, the authors propose a general approach to limit the interpretability of the functional representations by mapping the functional vocabulary with elementary structural blocks derived from bond graph theory. The physics-based reasoning approach is supported by a mathematical framework that is summarized in the article. The physics-based reasoning model is used for discovering the limitations of solutions in the form of internal contradictions and guiding the design ideation effort.

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Section: The Dacm Frameworkmentioning

confidence: 99%

Function modeling combined with physics-based reasoning for assessing design options and supporting innovative ideation

Mokhtarian

Coatanéa

Paris

2017

AIEDAM

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“…step 7: Find the contradictions The propagation of objectives in the causal graph can generate contradictions. 13 For example, the resulting objective of propagation can lead to variables that should simultaneously answer contradictory objectives. 14 step 8: Determine Design Directions and Potential Added Values The eighth step involves generating a virtual design of an experiment and computing the impact of dif- Figure 4.…”

Section: Fundamental Types Of Variables In a Modeling Problemmentioning

confidence: 99%

“…X High i n X Low i n 1 1 (13) The parameters V, A, µ, and ρ in Equation 10 that have a significant influence on Fd are ranked by order of magnitude in Figure 8. Figure 6.…”

Section: Fundamental Types Of Variables In a Modeling Problemmentioning

confidence: 99%

A Conceptual Modeling and Simulation Framework for System Design

et al. 2016

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The dimensional analysis conceptual modeling (DACM) framework is a conceptual modeling mechanism for lifecycle systems engineering. Originally developed for military projects, the DACM framework is now available for other applications, too. This powerful approach handles the specifying, discovering, validating, and reusing of building blocks as well as system behavior analysis in early development stages.

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Agile Vetting of Autonomous Systems

Ring¹

2016

INSIGHT

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Autonomous systems are already coping with complex, unpredictable situations. Some of these are raising red flags about unanticipated behaviors and unintended consequences (Miley 2015). Systems engineering practitioners must respond with new ways and means for creating and vetting autonomous systems. Systems have become so complex that most of their caretakers do not understand them. This means the system must be protected not only from cyber attacks by others but also from its own caretakers. Eventually every new line of code introduced into a system must be considered a cyber attack until proven otherwise. This article explores the challenges in vetting autonomous systems for sustainable system life, and offers a checklist for agile vetting throughout the lifecycle.

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Discovering the Real Problematic Situation: The First Aspect of Conceptual Design

Cited by 3 publications

References 3 publications

Function modeling combined with physics-based reasoning for assessing design options and supporting innovative ideation

Function modeling combined with physics-based reasoning for assessing design options and supporting innovative ideation

A Conceptual Modeling and Simulation Framework for System Design

Agile Vetting of Autonomous Systems

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