Physics-Based Reasoning in Conceptual Design Using a Formal Representation of Function Structure Graphs

Sen, Chiradeep; Summers, Joshua D.; Mocko, Gregory M.

doi:10.1115/1.4023488

Cited by 22 publications

(16 citation statements)

References 27 publications

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“…Evidently, other efforts have been made towards similar goals. Mostly, systems are described by functions/behaviors, leading to the topic of functional decomposition [5][6][7][8][9][10], which is mainly used in design synthesis. This allows automatic selection of components to fulfill certain requirements.…”

Section: Fig 1 Technology Evaluation and Selection Process [4]mentioning

confidence: 99%

Formalizing Technology Descriptions for Selection During Conceptual Design

Roelofs

Vos

2019

AIAA Scitech 2019 Forum

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actuator instead of hydraulic), to behaviors (e.g. a structural element doubling as electric conductor), and to entire systems (e.g. a fixed-wing aircraft as opposed to a helicopter). Previously, a review on technology selection [1] identified the following challenges with technology evaluation and selection: 1) The application of design tools is limited to a specific vehicle type, because the sizing method is fixed [2] 2) Flexibility and scalability of disciplinary analysis tools is lacking [2] 3) Parameterization of geometry proves challenging in a generalized sense (i.e. problem specific parameterizations are possible, but geometry parameterization that holds for any problem is challenging, at the least) 4) Modeling of technologies is usually avoided and replaced by impact factors 5) Extensive use of expert judgment raises challenges due to subjectivity, conservatism, overconfidence and lack of experts Additionally, from discussions with practitioners at Saab, the following challenges with technology selection in the conceptual design phase were identified: (i) non-performance metrics (e.g. the effect of improved human-machine interaction) need to be included, although prove difficult to define and quantify, (ii) technology descriptions are not yet meaningful, and cannot traverse from detailed to high-level descriptions, nor capture quantifiable effects and enabling fair comparison between technologies, (iii) finding the best technology portfolio without enumerating all possible combinations cannot yet be done objectively, (iv) the assessment of dependencies between technologies is too subjective, and (v) when uncertainty is quantified, decision making is impaired in case of wide uncertainty bands. Several conclusions can be drawn from these challenges. Metrics that are not quantifiable cannot be addressed, and hence, technologies affecting those have no meaning. Alternatively, additional analysis methods should be developed. Uncertainty should be associated with technology (and system) readiness level (both on impact and development time). Additionally, the amount of uncertainty should be limited or decision-making in the event of large uncertainty should be supported. Insight is more important at this stage than arriving at a most optimal result, hence preference is given to evaluating current possibilities, rather than performing optimization. Therefore, a structured and traceable solution to technology definition, evaluation and selection is sought. Conventionally, technology selection is performed by collecting technology TRL and IRL levels, describing their effects in an impact matrix and their compatibility in a Technology Compatibility Matrix (TCM). Such an approach was taken in the works by Amadori et al. [3, 4], who depict the process as in Figure 1. The data collection phase is essentially carried out completely using expert judgment, with limited traceability and objectivity.

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Section: Fig 1 Technology Evaluation and Selection Process [4]mentioning

confidence: 99%

Formalizing Technology Descriptions for Selection During Conceptual Design

Roelofs

Vos

2019

AIAA Scitech 2019 Forum

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“…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 ). Thus, it is possible that reasoning might be supported through human use and interpretation or through automated reasoning to infer information.…”

Section: Levels Of Comparisonmentioning

confidence: 99%

“…Another approach to compare representations examines the vocabulary, structure, expression, purpose, and abstraction (Summers & Shah, 2004). Expanding upon that research, we propose that the representation comparison should include, but not be limited to the following: scope: the domain for which the function modeling approach is intended (Nagel, Vucovich, et al, 2008); flexibility: the ability to modify and adapt the representation to address new problems (Regli et al, 2000); indexing: support access to the right (or useful) knowledge when needed (Goel & Bhatta, 2004); consistency: enforce physics and other consistencies (Sen et al, 2011 b ); translationabilty: tied to other engineering models (Nebel, 2000); behavior: ability of the representation to simulation behavior (Qian & Gero, 1996); and scalability: support both simple and complex problem types (Chiang et al, 2001). …”

Section: Comparison Across the Criteriamentioning

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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“…Where others start off with a FD, Yuan et al [9] developed a method to automate the FD process itself, which may be useful for end-users to specify their intentions when modeling a system. Sen et al [10][11][12] used function-structure graphs to describe the behavior of a system enabling physics-based reasoning on it. Although effective, it appears to only be applicable to mechanical and eletrical engineering domains, while continuum mechanics seem to pose a problem to this approach.…”

Section: Introductionmentioning

confidence: 99%

Automatically Generating the Technology Compatibility Matrix Using Graph Transformations

Roelofs

Vos

Amadori

et al. 2019

AIAA Aviation 2019 Forum

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One of the first stages during technology evaluation and selection is constructing the technology compatibility matrix, which indicates the compatibility of each pair in a technology portfolio. Construction of the technology compatibility matrix usually involves subjective expert judgment, which inhibits assessing each pair consistently and storing the decision rationale. Therefore, this study develops a method based on graph transformations, allowing for a formal theoretical description of technologies. In order to automate this process, two algorithms use these graph transformations to deduce technology compatibility and dependencies between technologies. The first checks whether changes made by a pair of transformations are compatible with each other. The second defines dependencies for each technology and attempts to resolve these using other technologies. The method is put into practice for a technology portfolio within Saab Aeronautics and the automatically generated compatibility matrix is compared to a pre-existing expert-judgment-based matrix for the same portfolio. Roughly 90% of the entries in the algorithm-based TCM are identical to the expert-based TCM. Therefore, the automated method is capable of capturing much of human reasoning in this context. However, in order to fully agree with human expertise, physics and design reasoning should be included to expand the scope of inference.

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Physics-Based Reasoning in Conceptual Design Using a Formal Representation of Function Structure Graphs

Cited by 22 publications

References 27 publications

Formalizing Technology Descriptions for Selection During Conceptual Design

Formalizing Technology Descriptions for Selection During Conceptual Design

Function in engineering: Benchmarking representations and models

Automatically Generating the Technology Compatibility Matrix Using Graph Transformations

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