Integrating the Knowledge About Functional Interdependencies Into a Parameter Management Approach

Toepfer, Ferdinand; Naumann, T.; Anderer, Janina; Vajna, Sándor

doi:10.21278/idc.2018.0222

Cited by 6 publications

(8 citation statements)

References 9 publications

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“…It can only be used for parametric design, and for the moment with continuous variables only. In our approach, discrete parameters are considered at the configuration level, and then, one model can be created for each configuration (Toepfer et al, 2018).…”

Section: Discussionmentioning

confidence: 99%

“…Firstly, one could use it to quickly predict if a design is feasible or not, or which constraints are violated. This, combined to the use of the tool presented in section 2 of Toepfer et al (2018) helps find solution boxes in the design space and therefore help deal with manufacturing tolerances and have robust designs. Moreover, it could be coupled with generative grammars, with grammars handling discrete parameters while our algorithm could map the solution space for each design.…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

A Metaheuristic for Solution Space Modelling

Poulain¹,

Naumann²,

Cardinal³

et al. 2018

Proceedings of the DESIGN 2018 15th International Design Conference

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To confront current market changes, Set-Based Design (SBD) should help carmakers improve time, cost and quality in early design phase. In a development process using ever more computer experiments, building predictive models of the solution space is key to implementing SBD. The present article proposes a new algorithm to build high quality predictive metamodels efficiently and tests it on several benchmark problems. Promising results are obtained. We believe such models could be used for several purpose in design: implementing SBD, optimisation and generating feasible concepts, among others.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

A Metaheuristic for Solution Space Modelling

Poulain¹,

Naumann²,

Cardinal³

et al. 2018

Proceedings of the DESIGN 2018 15th International Design Conference

Self Cite

View full text Add to dashboard Cite

show abstract

“…In general context of industrial or consumer products this provides a good foundation to describe and classify the architecture but in context of vehicle design, the design of all vehicles is modular in architecture and the interfaces in all designs are slot interface. Toepfer and Naumann (2016) 7 have described vehicle architecture as something which "describes the physical layout of a vehicle and the way it realizes its function by a given set of basic architectures parameters and modules". They, thus, suggest a method to develop a vehicle architecture in a CAD-PLM-database framework.…”

Section: Formulating a Multi-layered Framework For Automobile Archite...mentioning

confidence: 99%

Formulating a multi-layered framework for automobile architecture

Chandra

2022

Sci Rep

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Product architecture is defined as allocation of components to functions and their interfaces. But what makes this formulation insufficient for automobile architecture is their multi-layered allocation of components and the visual aspect of the architecture. This paper suggests, through an empirical study of two-wheeler models being manufactured by a prominent manufacturer, a multi-tiered framework along with a visual template for two-wheeler architecture which includes their visual schema as well. This paper investigates and demonstrates that this framework and template satisfy the requirements of product architecture by using examples from the domain of motorcycle design. Further, this paper investigates the utility of this framework for platforming strategy, innovation, adoption of new technology and standardization.

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“…To face and manage the growing complexity of HEV systems development, use of engineering system approach, and Model Based System Engineering (MBSE) tools helps to support design engineers in the system design process. 2 System architecture and system topology are widely used in system project development based on engineering system approach and must be introduced here. System topology represents system components connections, whereas system architecture is defined based on the topology enriched with component properties.…”

Section: Introductionmentioning

confidence: 99%

Optimal HEV cooling system design through a computer-aided virtual development chain

Baysset

Chessé

Chalet

et al. 2022

Proceedings of the Institution of Mechanical Engineers, Part D:

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To reduce CO2 and pollutant emissions, automotive manufacturers tend to turn increasingly toward electrified powertrains solutions, such as Hybrid Vehicles (HEV). Model Based System Engineering (MBSE) and computational tools are useful to support the development of HEV systems. System architectures need to be investigated to find optimal solutions regarding multiple possible goals constraints (fuel consumption, CO2 emissions, etc.). In this study, a novel global optimization framework is proposed to enable an automated cooling system optimization. The methodology starts with a set of components, design rules, and system requirements to search and automatically generate all the admissible cooling system architectures. System architectures are generated by a concept of decision tree. The decision tree algorithm is coupled to a design rules database to avoid exploring solutions that do not satisfy system requirements. Hydraulic models are automatically built for each system architecture generated to optimize head losses and minimize the pump consumption. For the most interesting solutions, 3D piping geometrical data (lengths, angles, and curve radius) are automatically drawn by a 3D optimization tool. Piping geometrical data are integrated in GT-SUITE thermo-hydraulic simulation models automatically built to evaluate the most interesting cooling system architectures generated. The best candidates are identified by a multi-criteria decision-making strategy. A first case of conventional vehicle cooling system is studied. Results on the given example indicate that the difference of energy consumption on WLTC cycle between the best performing architecture and the worst one is 69%.

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Integrating the Knowledge About Functional Interdependencies Into a Parameter Management Approach

Cited by 6 publications

References 9 publications

A Metaheuristic for Solution Space Modelling

A Metaheuristic for Solution Space Modelling

Formulating a multi-layered framework for automobile architecture

Optimal HEV cooling system design through a computer-aided virtual development chain

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