A trade-off function to tackle robust design problems in engineering

Quirante, Thomas; Sébastian, Patrick; Ledoux, Yann

doi:10.1080/09544828.2012.691160

Cited by 9 publications

(4 citation statements)

References 15 publications

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“…The common subjects that use ToCs to facilitate solutions of multi-objective optimisation problems are Manufacturing networks optimisation (Bitran and Morabito, 1999), scheduling (Catalão et al , 2008), capacity planning and resource allocation (Bretthauer et al , 2003) and inventory management (Grewal et al , 2014). The highlighted roles of ToCs within these studies are Decision support (Holtzman, 1984; Preetha Roselyn et al , 2014), data representation and visualisation (Abido, 2003; Rhyu and Kwak, 1988), best solution compromise (Avigad and Moshaiov, 2010; Mohagheghi et al , 2014; Zhao et al , 2011), comparing conflicting parameters (Dunning et al , 2014; Kuo et al , 2014), comparing solutions (Gardner and Everette, 1990; Quirante et al , 2013; Suwanruji and Enns, 2007) and feasible/infeasible area definition (Cao and Yang, 2004; Samarasinghe et al , 2013). However, these ToCs are developed by using the data generated by algorithms and mathematical calculations rather than real data, experience and knowledge.…”

Section: Review Of the Related Literaturementioning

confidence: 99%

Physics-based trade-off curves to develop a control access product in set-based concurrent engineering environment

Araci

Al-Ashaab²,

Almeida

2021

IJLSS

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Purpose This paper aims to present a process to generate physics-based trade-off curves (ToCs) to facilitate lean product development processes by enabling two key activities of set-based concurrent engineering (SBCE) process model that are comparing alternative design solutions and narrowing down the design set. The developed process of generating physics-based ToCs has been demonstrated via an industrial case study which is a research project. Design/methodology/approach The adapted research approach for this paper consists of three phases: a review of the related literature, developing the process of generating physics-based ToCs in the concept of lean product development, implementing the developed process in an industrial case study for validation through the SBCE process model. Findings Findings of this application showed that physics-based ToC is an effective tool to enable SBCE activities, as well as to save time and provide the required knowledge environment for the designers to support their decision-making. Practical implications Authors expect that this paper will guide companies, which are implementing SBCE processes throughout their lean product development journey. Physics-based ToCs will facilitate accurate decision-making in comparing and narrowing down the design-set through the provision of the right knowledge environment. Originality/value SBCE is a useful approach to develop a new product. It is essential to provide the right knowledge environment in a quick and visual manner which has been addressed by demonstrating physics knowledge in ToCs. Therefore, a systematic process has been developed and presented in this paper. The research found that physics-based ToCs could help to identify different physics characteristics of the product in the form of design parameters and visualise in a single graph for all stakeholders to understand without a need for an extensive engineering background and for designers to make a decision faster.

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Section: Review Of the Related Literaturementioning

confidence: 99%

Physics-based trade-off curves to develop a control access product in set-based concurrent engineering environment

Araci

Al-Ashaab²,

Almeida

2021

IJLSS

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“…Table 1 shows a simplified desirability scale with qualitative (linguistic) interpretation of desirability values, according to Harrington and other researchers [7,27,31].…”

Section: Determination Of Shape Parametersmentioning

confidence: 99%

Some applications of a novel desirability function in simultaneous optimization of multiple responses

Marinković¹

2021

FME Transactions

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In the framework of multi-response optimization techniques, the optimization methodology based on the desirability function is one of the most popular and most frequently used methodologies by researchers and practitioners in engineering, chemistry, technology and many other fields of science and technique. Numerous desirability functions have been introduced to improve the performance of this optimization methodology. Recently, a novel desirability function for multi-response optimization is proposed, which is smooth, nonlinear, and differentiable, and thus more suitable for applying some of the more efficient gradient-based optimization methods. This paper evaluates the performance of the proposed method through six real examples. After a comparative analysis of the results, it is shown that the proposed method in a certain measure outperforms the other competitive optimization methods.

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“…Literature suggests several strategies for studying the given trade-off beyond just identifying the actual Pareto Set. Besides examples such as aggressive/conservative trade-off strategies (Otto and Antonsson 1991) or the formulation as compromise design support problem (Mistree et al 1990), work on preference modelling under uncertainty (Quirante, Sebastian, and Ledoux 2013;Mourelatos and Liang 2005) is the most relevant in the context of this work. As decision support, Quirante, Sebastian, and Ledoux (2013) choose to qualify the degree of customer satisfaction based on desirability functions.…”

Section: Type II Robust Analysis -Optimisation Of Control Factorsmentioning

confidence: 99%

“…Besides examples such as aggressive/conservative trade-off strategies (Otto and Antonsson 1991) or the formulation as compromise design support problem (Mistree et al 1990), work on preference modelling under uncertainty (Quirante, Sebastian, and Ledoux 2013;Mourelatos and Liang 2005) is the most relevant in the context of this work. As decision support, Quirante, Sebastian, and Ledoux (2013) choose to qualify the degree of customer satisfaction based on desirability functions. In this way, the presented approach complements a strictly stochastic, hence aleatoric, description of control factors by a suitable strategy to treat epistemic uncertainty for aspects that cannot be described in more detail.…”

Section: Type II Robust Analysis -Optimisation Of Control Factorsmentioning

confidence: 99%

Integrated uncertainty management in parametric design and tolerancing

Dantan

Eifler

2021

Journal of Engineering Design

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The key purpose of robust design and tolerancing approaches is the management of uncertainty. Against this background, it is not surprising that there is a large overlap between the basic ideas and concepts in both fields. However, while sharing the same objective, the focus of the corresponding development phases is quite different; that is (i) the determination of solutions that react insensitive, in other words robust, to n oise factors -Robust parametric design; and (ii) the limitation of the effects of manufacturing imprecision by the specification of optimal tolerances -Tolerancing. As a consequence, there also is a significant gap between both concepts. Focusing on the improvement of design solutions, robustness is often related to uncertainty of not known designs or manufacturing processes. Due to the complexity of a largely matured solution, tolerancing tasks are usually based on previously specified, key characteristics or behavior models that are supposed perfect. Therefore, an overview of robust design and tolerancing is used to highlight the deficiencies, and to formalize a new classification of tolerance analysis issues based on the type of uncertainty considered. The proposed framework is based on Dempster-Shafer evidence theory and allows to efficiently perform statistical tolerance analyses under model imprecision.

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A trade-off function to tackle robust design problems in engineering

Cited by 9 publications

References 15 publications

Physics-based trade-off curves to develop a control access product in set-based concurrent engineering environment

Physics-based trade-off curves to develop a control access product in set-based concurrent engineering environment

Some applications of a novel desirability function in simultaneous optimization of multiple responses

Integrated uncertainty management in parametric design and tolerancing

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