Set based robust design of mechanical systems using the quantifier constraint satisfaction algorithm

Qureshi, Ahmed Jawad; Dantan, Jean‐Yves; Bruyère, Jérôme; Bigot, Régis

doi:10.1016/j.engappai.2010.02.003

Cited by 18 publications

(13 citation statements)

References 21 publications

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“…The constraint satisfaction problem "CSP" [6][7][8][9][10][11][12][13][14][15][16][17] is a programming paradigm that emerged in the 1980s to solve combinatorial problems of large sizes such as problems with planning and scheduling. It was intended to solve the mathematical problem that looks for states or objects satisfying a number of constraints.…”

Section: Constraint-satisfaction Problemmentioning

confidence: 99%

“…It requires supporting tools and methodologies adapted throughout the design process [1][2][3][4]. We are interested here in problems of optimal design [5][6][7] of components and we propose an improvement to these tools and methodologies using constraint satisfaction techniques [6][7][8][9][10][11][12][13][14][15][16][17]. Our approach is applied to an optimal sizing case of a compression spring [18][19][20][21][22][23][24][25][26][27].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Interval computation and constraint propagation for the optimal design of a compression spring for a linear vehicle suspension system

Trabelsi

Yvars

Louati

et al. 2015

Mechanism and Machine Theory

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Section: Constraint-satisfaction Problemmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Interval computation and constraint propagation for the optimal design of a compression spring for a linear vehicle suspension system

Trabelsi

Yvars

Louati

et al. 2015

Mechanism and Machine Theory

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“…As many design problems can be formulated under this concept, its use grew incrementally to support this particular engineering application [1,2,10].…”

Section: Csp As Knowledge Representation In Engineering Designmentioning

confidence: 99%

“…Those variables may have a finite set of possible values to take. Therefore, a modelling process exists to represent that knowledge in terms of P (V, D, C) 1 where V is the set of n design variables {V 1 , V 2 , . .…”

Section: Csp As Knowledge Representation In Engineering Designmentioning

confidence: 99%

“…There is evidence in the literature that constraintbased methods are widely used to model Engineering Design problems [1,[7][8][9]17]. The key point is that this knowledge, issued from expert's experience, should be properly elicitated, structured and stored in order to be reused to enrich future product developments.…”

Section: Csp As Knowledge Representation In Engineering Designmentioning

confidence: 99%

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Smart experience engineering to support collaborative design problems based on constraints modelling

Cálad-Álvarez

Mejía-Gutiérrez

Sanín

et al. 2014

Journal of Intelligent &Amp; Fuzzy Systems

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Engineering design is a knowledge intensive process. Experts' experiences from different product life-cycle stages play a key role in problem solving during design decision making by linking up knowledge to find better solutions for a specific design problem. Different approaches have been used to support Collaborative and Concurrent Product Design, such as Constraint Satisfaction Problem (CSP) modelling. Additionally, due to the need of capitalizing these experiences, the Set of Experience Knowledge Structure (SOEKS) is used to store and manage experiences of decision making process. However it was necessary to adapt the structure to property manage the constraints approach used in Engineering Design. This article presents a proposed adaptation to SOEKS structure in order to be able to store and reuse CSP design experiences for further re-use and analysis of multiple experiences. Finally, a case study is presented in order to show how the proposed approach can be used in the industrial applications.

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Survey on set‐based design (SBD) quantitative methods

Dullen

Verma

Blackburn

et al. 2021

Systems Engineering

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Product development efforts now more than ever are in need of methodologies that can address the challenges of increased system complexities, shortening time to market, increased demands in mass customization, market instabilities, geographical barriers, improved innovation, and adaptability to emerging technologies. To address these challenges most companies will need to make key decisions early in the product development life-cycle. In this early phase there are high levels of information uncertainty and information ambiguity. Under these circumstances many companies will converge too early to a point design (Point Based Design-PBD) which will lead to increased cost and schedule delays due to reworking the design later in the product development life cycle. To overcome these challenges many researchers have proposed the Set-Based Design (SBD) methodology. However, there has been limited guidance on how to define, reason, and narrow sets while improving the level of abstraction of the design.To address such concerns, a literature review was conducted. The contributions of this research include: (1) aggregated literature from over 100 sources on quantitative methods (QM) that has not been considered SBD but does support set-based thinking,(2) consolidated body of knowledge on QM to help industrial practitioners implement SBD, (3) defined gaps and opportunities for future research, and (4) defined strengths and limitations of QM and techniques to define, reason and narrow sets.

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Set based robust design of mechanical systems using the quantifier constraint satisfaction algorithm

Cited by 18 publications

References 21 publications

Interval computation and constraint propagation for the optimal design of a compression spring for a linear vehicle suspension system

Interval computation and constraint propagation for the optimal design of a compression spring for a linear vehicle suspension system

Smart experience engineering to support collaborative design problems based on constraints modelling

Survey on set‐based design (SBD) quantitative methods

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