Function in engineering: Benchmarking representations and models

Summers, Joshua D.; Eckert, Claudia; Goel, Ashok K.

doi:10.1017/s0890060417000476

Cited by 23 publications

(31 citation statements)

References 99 publications

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“…We use the benchmarking protocol from Summers et al (pp404‐406) to compare the proposed model against a well‐known latest model in literature—IBIS‐FAD . The benchmarking protocol involves a comparison of models across criteria (Table ) that fall into four characteristics: representation, modeling, cognitive, and reasoning.…”

Section: Support Deployment and Benchmarkingmentioning

confidence: 99%

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Modeling and structuring design rationale to enable knowledge reuse

Siddharth

Chakrabarti

Ranganath

2019

Systems Engineering

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We aim to lend insights into "what" constitutes design rationale and "how" to capture these. We restrict the scope of design rationale to the cases of failures observed while testing real engineered systems. We propose a model as an integration of a system hierarchy, a causal chain, and a causality model to cast multiple views of design rationale. The model is structured into databases using a newly introduced version of design structure matrices and supported using a web interface called CRIS 4 P, which is deployed at a space agency. We report a case example from the spacecraft domain and benchmark the proposed model against IBIS-FAD model. K E Y W O R D S complex systems, design rationale, failure assessment, knowledge representation, knowledge reuse, system failures 294 c ○ 2019 Wiley Periodicals, Inc. Systems Engineering. 2020;23:294-311. wileyonlinelibrary.com/journal/sys

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Section: Support Deployment and Benchmarkingmentioning

confidence: 99%

“…Second, we propose a system‐centric model to represent a case of failure in Section 3.2, a detailed use case of which is demonstrated in Section 3.3 with a NASA Satellite example. Third, we benchmark the proposed model against the IBIS‐FAD model in Section 4.3 using the criteria proposed by Summers et al …”

Section: Introductionmentioning

confidence: 99%

Modeling and structuring design rationale to enable knowledge reuse

Siddharth

Chakrabarti

Ranganath

2019

Systems Engineering

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“…The review of Srinivasan et al (2012) focused on the chronology of the development of function definitions and function representations, and deduced four views of function: level of abstraction, requirement–solution, system–environment, and intended–unintended. Summers et al (2013) proposed three dimensions of function modeling approaches: representation characteristics, supported cognitive dimension characteristics, and enabled reasoning activities. Eisenbart (2014) emphasized the disciplinary differences in function modeling approaches, that is, mechanical engineering, electrical engineering, software development, service development, mechatronic system development, product–service systems design, and systems engineering.…”

Section: Function Modeling Backgroundmentioning

confidence: 99%

“…Table 1 summarizes the evaluation of the SSFD function modeling framework against the key cognitive dimensions suggested by Summers et al (2013). A noteworthy point in relation to the analysis presented in Table 1 is that the sensitivity of the SSFD function modeling to the expertise of the analyst is an area that still requires research and development.…”

Section: Critical Evaluation Of Ssfd Using the Function Representatiomentioning

confidence: 99%

“…This paper presents underpinning research aiming to systematically address these requirements for the development of the SSFD methodology. The research aims to establish a rigorous, comprehensive, and coherent framework for function modeling of complex systems based on the SSFD, covering definitions and conventions for the representation of the elements of the SSFD functional model, a consistent methodology for the deployment of the SSFD, empirical validation of the SSFD function modeling methodology via a benchmark problem and an industrial case study, and a critical evaluation of the SSFD function modeling methodology based on the benchmarking framework introduced by Summers et al (2013). …”

Section: Introductionmentioning

confidence: 99%

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Function modeling using the system state flow diagram

Yildirim

Campean

Williams

2017

AIEDAM

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This paper introduces a rigorous framework for function modeling of complex multidisciplinary systems based on the system state flow diagram (SSFD). The work addresses the need for a consistent methodology to support solution-neutral function-based system decomposition analysis, facilitating the design, modeling, and analysis of complex systems architectures. A rigorous basis for the SSFD is established by defining conventions for states and function definitions and a representation scheme, underpinned by a critical review of existing literature. A set of heuristics are introduced to support the function decomposition analysis and to facilitate the deployment of the methodology with strong practitioner guidelines. The SSFD heuristics extend the existing framework of Otto and Wood (2001) by introducing a conditional fork node heuristic, to facilitate analysis and aggregation of function models across multiple modes of operation of the system. The empirical validation of the SSFD function modeling framework is discussed in relation to its application to two case studies: a benchmark problem (glue gun) set for the engineering design community; and an industrial case study of an electric vehicle powertrain. Based on the evidence from the two case studies presented in the paper, a critical evaluation of the SSFD function modeling methodology is discussed based on the function benchmarking framework established by Summers et al. (2013), considering the representation, modeling, cognitive, and reasoning characteristics. The significance of this paper is that it establishes a rigorous reference framework for the SSFD function representation and a consistent methodology to guide the practitioner with its deployment, facilitating its impact to industrial practice.

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Evaluating the effectiveness of functional decomposition in early-stage design: development and application of problem space exploration metrics

She,

Belanger,

Bartels

2024

Res Eng Design

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This paper aims to explore metrics for evaluating the effectiveness of functional decomposition methods regarding problem space exploration at the early design stage. Functional decomposition involves breaking down the main purpose of a complex problem or system into a set of more manageable sub-functions, leading to a clearer understanding of the problem space and its various aspects. While various metrics have been used to evaluate functional decomposition outcomes, little literature has focused on assessing its effectiveness in problem space exploration. To address the gap, this research introduces three metrics for problem space evaluation defined by functional models: quantity of unique functions (M1), breadth and depth of the hierarchical structure (M2), and relative semantic coverage ratio of the problem space (M3). An example study is conducted to illustrate the evaluation process, comparing functional analysis with and without explicit human-centric considerations using a power screwdriver as a case product. The analysis in the example study reveals that the breadth of the hierarchical structure (part of M2) is marginally larger in the condition with explicit human-centric considerations (Condition A) compared to the condition without such considerations (Condition B). However, no significant differences are observed in terms of other metrics. The qualitative analysis based on semantic comparisons suggests that Condition A facilitates participants in generating a diverse set of functions supporting user safety requirements more effectively than Condition B. Overall, the example study demonstrates the evaluation process for each metric and discusses their nuances and limitations. By proposing these metrics, this research contributes to benchmarking and evaluating the effectiveness of different methods in promoting functional analysis in engineering design. The metrics provide valuable insights into problem space exploration, offering designers a better understanding of the efficacy of their functional decomposition methods in early design stages. This, in turn, fosters more informed decision-making and contributes to the advancement of functional analysis methodologies in engineering design practices.

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Function in engineering: Benchmarking representations and models

Cited by 23 publications

References 99 publications

Modeling and structuring design rationale to enable knowledge reuse

Modeling and structuring design rationale to enable knowledge reuse

Function modeling using the system state flow diagram

Evaluating the effectiveness of functional decomposition in early-stage design: development and application of problem space exploration metrics

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