Soft computing in engineering design – A review

Saridakis, Kostas M.; Dentsoras, A.J.

doi:10.1016/j.aei.2007.10.001

Cited by 113 publications

(66 citation statements)

References 117 publications

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“…Preference models are related to different types of knowledge ranging from analytical knowledge derived from physics or economics to design rules based on expertise or case-based reasoning (Mitra & Basak, 2005;Saridakis & Dentsoras, 2008). Every type of model may have a fair degree of accuracy and a comparable amount of prediction capability (Vernat, Nadeau, & Sébastian 2010), however, they can be distinguished by their levels of subjectivity.…”

Section: Scientific Backgroundmentioning

confidence: 99%

“…A priori statements assume that designers are able to translate their knowledge through rational rules and mathematical relations, whereas, in essence, some preferences are difficult to express. Therefore, soft computing and fuzzy logic is receiving considerable attention for modeling preferences (Antonsson & Sebastian, 2005;Hung, Julian, Chien, & Jin, 2010;Saridakis & Dentsoras, 2008) due to the intrinsic fuzziness in designers' preferences. Similar approaches consist in using desirability functions (Harrington, 1965) to translate design requirements and designers' preferences into relaxed constraints.…”

Section: Scientific Backgroundmentioning

confidence: 99%

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Linking objective and subjective modeling in engineering design through arc-elastic dominance

Sébastian

Ledoux

Collignan

et al. 2012

Expert Systems with Applications

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is an open access repository that collects the work of Arts et Métiers ParisTech researchers and makes it freely available over the web where possible. Engineering design in mechanics is a complex activity taking into account both objective modeling processes derived from physical analysis and designers' subjective reasoning. This paper introduces arc-elastic dominance as a suitable concept for ranking design solutions according to a combination of objective and subjective models. Objective models lead to the aggregation of information derived from physics, economics or eco-environmental analysis into a performance indicator. Subjective models result in a confidence indicator for the solutions' feasibility. Arc-elastic dominant design solutions achieve an optimal compromise between gain in performance and degradation in confidence. Due to the definition of arcelasticity, this compromise value is expressive and easy for designers to interpret despite the difference in the nature of the objective and subjective models. From the investigation of arc-elasticity mathematical properties, a filtering algorithm of Pareto-efficient solutions is proposed and illustrated through a design knowledge modeling framework. This framework notably takes into account Harrington's desirability functions and Derringer's aggregation method. It is carried out through the re-design of a geothermal air conditioning system.

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Section: Scientific Backgroundmentioning

confidence: 99%

Section: Scientific Backgroundmentioning

confidence: 99%

Linking objective and subjective modeling in engineering design through arc-elastic dominance

Sébastian

Ledoux

Collignan

et al. 2012

Expert Systems with Applications

View full text Add to dashboard Cite

show abstract

“…In industrial processes, a typical occurrence in sub-contracting mechanical design, design activity is based on companies' knowhow as well as on designer's imprecise knowledge [1]. These processes require several iterations between product design and simulation, starting from a predefined solution.…”

Section: Introductionmentioning

confidence: 99%

“…The majority of product design optimization problems are regarded as being ''multi-objective'' [1]: satisfying one of the product's performance criteria, which are related to physical observation variables, is linked to the performance of the other observation variables. Ullman proposes a list of the main elements that must be taken into account in making decisions for this kind of problem: design alternatives and human preferences [5].…”

Section: Introductionmentioning

confidence: 99%

Arc-elasticity and hierarchical exploration of the neighborhood of solutions in mechanical design

Collignan

Sébastian

Pailhès

et al. 2012

Advanced Engineering Informatics

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is an open access repository that collects the work of Arts et Métiers ParisTech researchers and makes it freely available over the web where possible. In most industrial design processes, the approaches used to obtain a design solution that best fits the specification requirements result in many iterations of the ''trial-and-error'' type, starting from an initial solution. In this paper, a method is proposed to formalize the decision process in order to automate it, and to provide optimal design solutions. Two types of knowledge are formalized. The first expresses the satisfaction of design objectives, relating to physical behaviors of candidate design solutions. This formalization uses three models, an observation one, an interpretation one and an aggregation one; every design solution is qualified through a single performance variable (a single objective function). The second model is related to modifications that may or may not be applicable to the pre-existing solution. The Designer is often able to define preferences concerning design variables. Some modifications related to this pre-existing solution, can be preferred to other ones. A hierarchy of design variables is proposed to formalize these preferences. The concept of arc-elasticity is introduced as a post-processing indicator to qualify candidate solutions through a trade-off between the performance improvement and their relative distances to the initial solution. The proposed method is used and applied to a riveted assembly, and a genetic algorithm is used to identify optimal solutions.

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“…In the latter case, Multi-objective Evolutionary Algorithms (MOEAs) have become a valuable tool to approximate the Pareto front for non-convex, non-linear and constrained optimisation instances [5,6]. They have been used with success in several control systems [7] and engineering design [8] areas.…”

Section: Introductionmentioning

confidence: 99%

Controller tuning using evolutionary multi-objective optimisation: Current trends and applications

Reynoso-Meza

Blasco

Sanchis

et al. 2014

Control Engineering Practice

118

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Control engineering problems are generally multi-objective problems; meaning that there are several specifications and requirements that must be fulfilled. A traditional approach to calculate a solution with the desired trade-off is to define an optimisation statement. Multi-objective optimisation techniques deal with this problem from a particular perspective searching for a set of potentially preferable solutions; the designer may then analyse the trade-off among them, and select the best solution according to his/her preferences. In this paper, this design procedure based on evolutionary multiobjective optimisation (EMO) is presented and significant applications on controller tuning are commented on. Through this paper it is noticed that EMO research has been developing towards different optimization statements, but such statements are not commonly used on controller tuning. Therefore gaps between EMO research and EMO applications on controller tuning are detected and suggested as potential trends for research.

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Soft computing in engineering design – A review

Cited by 113 publications

References 117 publications

Linking objective and subjective modeling in engineering design through arc-elastic dominance

Linking objective and subjective modeling in engineering design through arc-elastic dominance

Arc-elasticity and hierarchical exploration of the neighborhood of solutions in mechanical design

Controller tuning using evolutionary multi-objective optimisation: Current trends and applications

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