An Assignment of Methods to Analyze Uncertainty in Different Stages of the Development Process

Eifler, Tobias; Engelhardt, Roland; Mathias, Johannes; Kloberdanz, Hermann; Birkhofer, Herbert

doi:10.1115/imece2010-39126

Cited by 7 publications

(6 citation statements)

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“…Deviations from the ideal function, disturbance parameters and their influences are only rudimentarily known and initially not taken into account in solution synthesis. The current robust design methods therefore focus mainly on the 'embodiment design' and 'detail design', where more concrete models of the developed product are already available [52,124].…”

Section: Robust Design In the Early Phases Of Product Developmentmentioning

confidence: 99%

Strategies for Mastering Uncertainty

Pfetsch

Abele

Altherr

et al. 2021

Springer Tracts in Mechanical Engineering

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This chapter describes three general strategies to master uncertainty in technical systems: robustness, flexibility and resilience. It builds on the previous chapters about methods to analyse and identify uncertainty and may rely on the availability of technologies for particular systems, such as active components. Robustness aims for the design of technical systems that are insensitive to anticipated uncertainties. Flexibility increases the ability of a system to work under different situations. Resilience extends this characteristic by requiring a given minimal functional performance, even after disturbances or failure of system components, and it may incorporate recovery. The three strategies are described and discussed in turn. Moreover, they are demonstrated on specific technical systems.

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Section: Robust Design In the Early Phases Of Product Developmentmentioning

confidence: 99%

Strategies for Mastering Uncertainty

Pfetsch

Abele

Altherr

et al. 2021

Springer Tracts in Mechanical Engineering

Self Cite

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“…Recent literature in engineering design has acknowledged that system design is rife with epistemic uncertainty, 14–16 and engineers make design decisions based on subjective beliefs about the true state of the system design 17–19 . Unlike aleatory uncertainty, where the uncertainty arises from physical variations in the underlying process, epistemic uncertainty arises due to a lack of knowledge about the current state of the design, or other aspects of the design process 20,21 .…”

Section: Background and Motivationmentioning

confidence: 99%

An evaluation of the optimality of frequent verification for vertically integrated systems

Kulkarni

Salado

et al. 2020

Systems Engineering

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Verification activities, such as inspection, testing, analysis, and demonstration, improve one's confidence in the system meeting the system requirements during the development process. Frequent verification is often advocated as a strategy that minimizes costs of rework over the entire design process, where frequent verification involves verifying after any change in the design. However, this strategy is yet to be validated. In this paper, we develop a belief‐based model of verification in systems design to determine the conditions under which frequent verification is an optimal strategy for a vertically integrated organization. Our model uses belief distributions to capture the organization's dynamic confidence in the system design meeting a requirement of interest during the development process. It also captures the organization's dynamic confidence in the correctness of its development activities (or design process) as a function of past verification activities and current system maturity. The analysis of our model shows that frequent verification is a cost‐minimizing strategy for any level of belief in satisfying the requirement only when the organization has high confidence in the correctness of its design activities and the expected cost to rework a faulty design is greater than the costs to set up the verification activities throughout the development process. Otherwise, strategies with infrequent verification are superior. Our work contributes to the growing body of literature on the theoretical foundations of systems engineering and engineering design and seeks to provide practitioners with a means to determine optimal verification strategies.

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“…The aleatoric uncertainty stems from the stochastic effects such as the random noise and measurement error. Aleatoric uncertainty is quantifiable through stochastic terms and the probability theory (Eifler et al, 2010). A challenge with the aleatoric uncertainty is that we cannot mitigate it by additional data or analysis.…”

Section: Effects Of Uncertainties On Product and System Designmentioning

confidence: 99%

Design Optimization for Sustainable Products Under Users' Preference Changes

Afshari

Peng

2016

Journal of Computing and Information Science in Engineering

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Decisions made in the early design phase enormously contribute to the performance of a product during its life cycle. Since users' preferences may change over time, a product design should be revised under the preference change. Providing accurate data for designers ensures an optimal decision for product design; this research presents a new method to assess effects of the quantified changes on product cost and development time. In addition, two models to optimize design under unexpected disturbances are proposed. Normally, optimal parameters require several search iterations in design process before finalizing a product. The design time in terms of number of iterations can be reduced by adding resources in each iteration using modern control engineering methods. However, adding resources will increase the design cost. The proposed method in this research minimizes the total product design cost and environmental impacts under changes of users' preferences. The method is validated using an example of the smartphone design. The research novelty is a method of applying quantified changes of external disturbances (such as changes in users' preferences) in the design process, addressing a real problem in industry, and proposing optimal models of products for reduced cost and environmental impacts.

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An Assignment of Methods to Analyze Uncertainty in Different Stages of the Development Process

Cited by 7 publications

References 0 publications

Strategies for Mastering Uncertainty

Strategies for Mastering Uncertainty

An evaluation of the optimality of frequent verification for vertically integrated systems

Design Optimization for Sustainable Products Under Users' Preference Changes

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