Identifying Critical Functions for Use Across Engineering Design Domains

Lucero, Briana; Viswanathan, Vimal; Linsey, Julie; Turner, Cameron J.

doi:10.1115/1.4028280

Cited by 24 publications

(20 citation statements)

References 21 publications

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“…Several studies have been undertaken to study the authorship and consistency and interpretability of models (Kurfman et al, 2003; Caldwell, Ramachandran, et al, 2012; Caldwell, Thomas, et al, 2012), as well as elucidating the correctness of model construction (Nagel et al, 2015). Alternatively, some approaches have been proposed that automatically reason on function models from database collections (Lucero et al, 2014; Patel, Andrews, et al, 2016; Sridhar et al, 2016). Finally, some approaches entail the support of first principle based physics reasoning (Goel et al, 2009; Sen et al, 2011 b , 2013 a ).…”

Section: Levels Of Comparisonmentioning

confidence: 99%

Function in engineering: Benchmarking representations and models

Summers

Eckert

Goel

2017

AIEDAM

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This paper presents the requirements and needs for establishing a benchmarking protocol that considers representation characteristics, supported cognitive criteria, and enabled reasoning activities for the systematic comparison of function modeling representations. Problem types are defined as reverse engineering, familiar products, novel products, and single-component systems. As different modeling approaches share elements, a comparison of modeling approaches on multiple levels was also undertaken. It is recommended that researchers and developers of function modeling representations collaborate to define a canonically acceptable set of benchmark tests and evaluations so that clear benefits and weaknesses for the disparate collection of approaches can be compared. This paper is written as a call to action for the research community to begin establishing a benchmarking standard protocol for function modeling comparison purposes. This protocol should be refined with input from developers of the competing approaches in an academically open environment. At the same time, the benchmarking criteria identified should also serve as a guide for validating a modeling approach or analyzing its failure.

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Section: Levels Of Comparisonmentioning

confidence: 99%

Function in engineering: Benchmarking representations and models

Summers

Eckert

Goel

2017

AIEDAM

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“…Functions with many pathways between them have a higher probability, when all propagation paths are equally likely to occur, as compared to functions with few pathways between them. However, functions with few pathways between them may also be indicative of critical flow pathways [23]. Critical flow pathways with few or no redundancy could then be redesigned with additional redundancy or risk mitigation may be imposed via other means (e.g., high reliability safety system designation, reduced reliance on critical flow path, etc.).…”

Section: Developing Failure Propagation Flowsmentioning

confidence: 99%

A Graph Theory Approach to Functional Failure Propagation in Early Complex Cyber‐Physical Systems (CCPSs)

O’Halloran

Papakonstantinou

Giammarco³

et al. 2017

INCOSE International Symp

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This paper presents a framework to quantify failure propagation potential for complex, cyber‐physical systems (CCPSs) during the conceptual stages of design. This method is referred to as the Function Failure Propagation Potential Methodology (FFPPM). This research is motivated by recent trends in engineering design. As systems become increasingly connected, an open area of research for CCPSs is to move reliability and failure assessments earlier in the engineering design process. This allows practitioners to make decisions at a point in the design process where the decision has a high impact and a low cost. Standard methods are limited by the availability of data and often rely on detailed representations of the system. As such, they have not addressed failure propagation in the functional design prior to selecting candidate architectures. To develop the metrics, graph theory is used to model and quantify the connectedness of the functional block diagram (FBD). These metrics quantify (1) the summation of the reachability matrix and (2) the summation of the number of paths between nodes (functions within system models) i and j for all i and j. From a practical standpoint, these metrics quantify the reachability between functions in the graph and the number of paths between functions defines the failure propagation potential of that failure. The unique contribution of this research is to quantify failure propagation potential during conceptual design prior to selecting candidate architectures. The goal of these metrics is to produce derived system requirements, based on an analysis, that focus on minimizing the impact of failures.

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“…The Design-Analogy Performance Parameter System (D-APPS) is a tool that computationally identifies design analogies (Lucero, 2014;Lucero et al, 2014Lucero et al, , 2016. D-APPS, in its simplest form, is an analogy "search engine" that returns analogies to the engineer based on the specific performance parameters and critical chain models of the design problem and the analogical solutions.…”

Section: Function Modeling For Design Analogiesmentioning

confidence: 99%

Transforming functional models to critical chain models via expert knowledge and automatic parsing rules for design analogy identification

Agyemang

Linsey

Turner

2017

AIEDAM

Self Cite

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Critical chains composed of critical flows and functions have been demonstrated as an effective qualitative analogy retrieval approach based on performance metrics. In prior work, engineers used expert knowledge to transform functional models into critical chain models, which are abstractions of the functional model. Automating this transformation process is highly desirable so as to provide for a robust transformation method. Within this paper, two paradigms for functional modeling abstraction are compared. A series of pruning rules provide an automated transformation approach, and this is compared to the results generated previously through an expert knowledge approach. These two approaches are evaluated against a set of published functional models. The similarity of the resulting transformation of the functional models into critical chain models is evaluated using a functional chain similarity metric, developed in previous work. Once critical chain models are identified, additional model evaluation criteria are used to evaluate the utility of the critical chain models for design analogy identification. Since the functional vocabulary acts as a common language among designers and engineers to abstract and represent critical design artifact information, analogous matching can be made about the functional vocabulary. Thus, the transformation of functional models into critical chain models enables engineers to use functional abstraction as a mechanism to identify design analogies. The critical flow rule is the most effective first step when automatically transforming a functional model to a critical chain model. Further research into more complex critical chain model architectures and the interactions between criteria is merited.

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Identifying Critical Functions for Use Across Engineering Design Domains

Cited by 24 publications

References 21 publications

Function in engineering: Benchmarking representations and models

Function in engineering: Benchmarking representations and models

A Graph Theory Approach to Functional Failure Propagation in Early Complex Cyber‐Physical Systems (CCPSs)

Transforming functional models to critical chain models via expert knowledge and automatic parsing rules for design analogy identification

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