Management of product characteristics uncertainty based on formal logic and characteristics properties model

Dantan, Jean‐Yves; Qureshi, Ahmed Jawad; Antoine, Jean-François; Eisenbart, Boris; Blessing, Luciënne

doi:10.1016/j.cirp.2013.03.077

Cited by 19 publications

(14 citation statements)

References 8 publications

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“…Using this logic, we are able to include a large number of relevant documents within this review that may have otherwise been missed, enabling a more comprehensive assessment of SBD. We refer to this set of documents as Group B from this point forward 1,66–122 . We provide the complete listing for all Group B documents in Table 2.…”

Section: Methodsmentioning

confidence: 99%

Set‐based design: The state‐of‐practice and research opportunities

Shallcross¹,

Parnell²,

Pohl

et al. 2020

Systems Engineering

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Increasing system complexity has provided the impetus to develop new and novel systems engineering methodologies. One of these methodologies is set‐based design (SBD), a concurrent design methodology well suited for complex systems subject to significant uncertainty. Since the 1990s, numerous private, public, and defense sector design programs have successfully implemented SBD. However, concerns regarding SBD's complexity, tendency toward qualitative methods, and lack of quantitative tools have limited its use. To address these issues, our research surveys 122 refereed journal articles and conference papers to assess SBD's state‐of‐practice and identify relevant research opportunities. To accomplish these tasks, we perform a structured literature review to identify and assess relevant and influential research. We found that SBD's state‐of‐practice relies heavily upon decision and tradespace analysis with increasing emphasis on uncertainty modeling and MBSE. We found that the majority of SBD research consists of quantitative methodologies focusing on component and small system applications. We also found that complex system applications used mostly qualitative methodologies. We identify SBD research opportunities for requirements development, MBSE, uncertainty modeling, multiresolution modeling, adversarial analysis, and program management. Finally, we recommend the development of a comprehensive SBD methodology and toolkit, suited for complex system design across all stages of the product development life cycle.

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Section: Methodsmentioning

confidence: 99%

Set‐based design: The state‐of‐practice and research opportunities

Shallcross¹,

Parnell²,

Pohl

et al. 2020

Systems Engineering

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“…Select design(s) with best expected outcome for further development The steps in this procedure are very general in order to ensure their applicability across a range of manufacturing systems and products. The utility of steps such as these has been well documented by others [e.g., Helu et al, 2011;Dantan et al, 2013]. The remainder of this section will be dedicated to describing how these steps may be implemented in the development of energy systems, with emphasis on how uncertainty might be considered.…”

Section: Frameworkmentioning

confidence: 98%

Energy System Design to Maximize Net Energy Production Considering Uncertainty in Scale-up: A Case Study in Artificial Photosynthesis

Walczak¹,

Hutchins²,

Dornfeld³

2014

Procedia CIRP

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Increasing energy demands coupled with decreasing resources and increased concerns about the long-term impacts of energy production are driving the development and full-scale implementation of new energy production systems. In general, several considerations must be addressed to move technology from the prototype phase to complete deployment, while ensuring adequate return on investment. These considerations include: availability of capable manufacturing processes, appropriate material selection, and supply chain resiliency. For new energy production systems, it is also important to evaluate net energy production and life cycle impacts. There is significant uncertainty associated with each of these considerations. This paper proposes a framework for maximizing net energy production in full-scale systems considering these uncertainties. The utility of the framework is demonstrated through a case study focused on an analysis of prototyping and scale-up of artificial photosynthesis as a solar-fuel generator.

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“…This is due to presence of ambiguity and epistemic uncertainty in earlier phases, which hinders the application of concrete numerical models in earlier phases. A few models aim to resolve early stage uncertainty through integration of abstract modelling techniques such as function modelling with metamodels of systems with uncertainty integration [83,103,204]. Some approaches have addressed the integration of robust design in early phases with downstream phases while considering the impact of the design parameters on the sensitivity to variation as well as cost [101].…”

Section: Findmentioning

confidence: 99%

“…There are limited applications of tolerancing in upstream design process. Dantan et al [83,237] have provided an integrated method to consider robust design and tolerance design activities. Ebro and Howard [101] have related tolerance sensitivity analysis to a products core function as well as manufacturability and success in the early design phases.…”

Section: Tolerance Designmentioning

confidence: 99%