Accuracy and Precision Analysis of the Graph Complexity Connectivity Method

Sridhar, Sudarshan; Fazelpour, Mohammad; Gill, Amaninder Singh; Summers, Joshua D.

doi:10.1016/j.procir.2016.02.029

Cited by 14 publications

(16 citation statements)

References 7 publications

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“…The inferencing support (surrogate) explored here is the ability of a representation to support the prediction of a market value for a product based on the functional description. This work builds on previous work that explored market value prediction (Mathieson et al, 2011), the accuracy of prediction models (Sridhar et al, 2016 a ), and the precision of these prediction models (Mohinder et al, 2014, 2016). The research questions are the following: Can the value of a representation be used to compare two similar function representations? Can the value of a representation be associated with specific pruning rules? …”

Section: Research Motivation: Comparative Studies On Representation Imentioning

confidence: 92%

“…Since changing the abstraction in a design representation can possibly lead to it being classified as a different design representation and the pruning rules themselves relate to both the grammar and the vocabulary allowed (Summers & Shah, 2004), it is important to verify the impact on the original set of function structures of different abstractions carried out in the following section. In order to provide a uniform base for comparison of different representation, the same set of 20 consumer products were used for this study and are similar to the sets used in previous work (Mathieson et al, 2011; Mohinder et al, 2014; Gill & Summers, 2016; Sridhar et al, 2016 b ).…”

Section: Abstraction By Pruning As a Distinguishing Characteristic Ofmentioning

confidence: 99%

“…From the previous discussions on pruning and comparison of error values obtained from the graph connectivity complexity method, which will be discussed further in Section 7, it is clear that there is a marked difference in how well each of the various pruned function structure sets behave as prediction models. This method has been used for market price prediction (Mathieson et al, 2011; Mohinder et al, 2014; Sridhar et al, 2016 b ), for assembly time estimation (Owensby et al, 2012; Namouz & Summers, 2013, 2014), for assembly quality defect prediction (Patel et al, 2016), and for using requirements to predict life cycle costs (Visotsky et al, 2017). As per the classification framework developed in Summers and Shah (2004), a design representation can be classified as being distinct on the basis of these five characteristics: vocabulary, structure (grammar), expression, purpose, and abstraction of the representation.…”

Section: Classification Of Pruned Function Structures As Independent mentioning

confidence: 99%

See 2 more Smart Citations

Comparing function structures and pruned function structures for market price prediction: An approach to benchmarking representation inferencing value

Gill

Summers

Turner

2017

AIEDAM

Self Cite

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Benchmarking function modeling and representation approaches requires a direct comparison, including the inferencing support by the different approaches. To this end, this paper explores the value of a representation by comparing the ability of a representation to support reasoning based on varying amounts of information stored in the representational components of a function structure: vocabulary, grammar, and topology. This is done by classifying the previously developed functional pruning rules into vocabulary, grammatical, and topological classes and applying them to function structures available from an external design repository. The original and pruned function structures of electromechanical devices are then evaluated for how accurately market values can be predicted using the graph complexity connectivity method. The accuracy is found to be inversely related to the amount of information and level of detail. Applying the topological rule does not significantly impact the predictive power of the models, while applying the vocabulary rules and the grammar rules reduces the accuracy of the predictions. Finally, the least predictive model set is that which had all rules applied. In this manner, the value of a representation to predict or answer questions is quantified.

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Section: Research Motivation: Comparative Studies On Representation Imentioning

confidence: 92%

Section: Abstraction By Pruning As a Distinguishing Characteristic Ofmentioning

confidence: 99%

Section: Classification Of Pruned Function Structures As Independent mentioning

confidence: 99%

See 1 more Smart Citation

Comparing function structures and pruned function structures for market price prediction: An approach to benchmarking representation inferencing value

Gill

Summers

Turner

2017

AIEDAM

Self Cite

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“…They demonstrated with an example that requirement based engineering approach does not result in the best possible design solution. The limitations of requirement-based engineering design are also addressed by ( Collopy, 2007;Lee, 2014;Shankar, Fazelpour and Summers, 2015;Yoder et al, 2015;Fazelpour, Shankar and Summers, 2016;Sridhar et al, 2016). Much of this research, however, used either case studies or qualitative research methods to point out the challenges of requirement-based engineering design.…”

Section: Literature Reviewmentioning

confidence: 99%

A Game‐Theoretical Framework for Coordination in Requirement‐Based Engineering Design

Yazdani

Huang

2018

INCOSE International Symp

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Requirement‐based engineering design is a common practice that provides a definition of what is the desirable design solution. However, requirements define what constitutes the acceptable design from what is not, and provide a little guidance on the best design solution. Requirement‐based engineering with team coordination, on the other hand, allows design teams to exchange resources to best manage their resources. This may lead to the better design outcome. This paper investigates the benefits of coordination in requirement‐based engineering design. We apply game theory to formulate mathematical models of design problem with and without team coordination and demonstrate the mathematical framework on an illustrative example. The results indicate that we may not always get the system‐wide best performance in the requirement‐based engineering design while coordination among design teams can reduce the total cost of the system. Also, the numerical results capture the relation between team's effort level and system's cost coefficient.

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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

Self Cite

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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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Accuracy and Precision Analysis of the Graph Complexity Connectivity Method

Cited by 14 publications

References 7 publications

Comparing function structures and pruned function structures for market price prediction: An approach to benchmarking representation inferencing value

Comparing function structures and pruned function structures for market price prediction: An approach to benchmarking representation inferencing value

A Game‐Theoretical Framework for Coordination in Requirement‐Based Engineering Design

Function in engineering: Benchmarking representations and models

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