A Generative Design and Drag Coefficient Prediction System for Sedan Car Side Silhouettes based on Computational Fluid Dynamics

Günpinar, Erkan; Coşkun, Umut Can; Ozsipahi, Mustafa; Gunpinar, Serkan

doi:10.1016/j.cad.2019.02.003

Cited by 52 publications

(20 citation statements)

References 28 publications

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“…Compared with the exiting method, the one proposed in this research is able to provide multiple product appearances by adjusting only one parameter; however, it cannot be used for products that have complex curves. In the traditional shape evaluation method [37][38][39][40][41][42][43][44][45], adjective degrees are determined by respondents or experts after observing product entities or pictures. A product entity or picture is required to rate adjective degrees.…”

Section: Discussionmentioning

confidence: 99%

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Parametric Design and Kansei Engineering in Goblet Styling Design

2020

Mathematical Problems in Engineering

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In this study, we developed a computer-aided product design method for goblet styling design based on two methods. The first was parametric design derived from an adjustable cam mechanism, which was used for shape generation, and the second was Kansei engineering, which was used for shape evaluation. In the shape generation method, motion curves from an adjustable cam were used. Designers can collect feature point data from existing products to define the boundary conditions of adjustable cam motion equations; furthermore, adjustable motion curves allow parametric design. Through adjusting a single parameter, motion curves were changed to be used as projective curves for the styling design of goblets. Then, a coordinate transformation method was applied to support the three-dimensional styling design of goblets. In the shape evaluation method, some goblet stylings were regularly selected to determine adjective degrees by production design experts. Adjective degrees for goblets that had not been selected were determined through interpolation. Market demand was defined as the preference of customers for specific adjective degrees for goblets.

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Section: Discussionmentioning

confidence: 99%

“…In addition, a generative design method was designed based on parameters. It is a type of styling method that imitates nature and uses nonlinear algorithms to create infinite changes [39,40].…”

Section: Introductionmentioning

confidence: 99%

Parametric Design and Kansei Engineering in Goblet Styling Design

2020

Mathematical Problems in Engineering

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“…Though not stated, this is perhaps because the fully-automated generation on parametric CAD models was disappointing. The same integrated approach for generating multiple options is in found Gunpinar et al's [62] system for car profile designs which combines aesthetic evaulation with performance information from Computational Fluid Dynamics (CFD).…”

Section: The Development Of Generative Product Design Systemsmentioning

confidence: 99%

Progress and Challenges in Generative Product Design: A Review of Systems

Mountstephens

Teo

2020

Computers

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Design is a challenging task that is crucial to all product development. Advances in design computing may allow machines to move from a supporting role to generators of design content. Generative Design systems produce designs by algorithms and offer the potential for the exploration of vast design spaces, the fostering of creativity, the combination of objective and subjective requirements, and the revolutionary integration of conceptual and detailed design phases. The application of generative methods to the design of discrete, physical, engineered products has not yet been reviewed. This paper reviews the Generative Product Design systems developed since 1998 in order to identify significant approaches and trends. Systems are analyzed according to their primary goal, generative method, the design phase they focus on, whether the generation is automatic or interactive, the number of design options they generate, and the types of design requirements involved in the generation process. Progress using this approach is recognized, and a number of challenges that must be addressed in order to achieve widespread acceptance are identified. Possible solutions are offered, including innovative approaches in Human–Computer Interaction.

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“…For instance, for n geometric parameters each with I intervals, Fractal provides n I design options. Recently, Gunpinar et al [6] proposed a generative design system for the generation of Sedan Car Side Silhouettes. They also integrated their system with machine learning techniques to predict the drag coefficients of Car Silhouettes during design space exploration.…”

Section: Generative Designmentioning

confidence: 99%

Evolving a Psycho-Physical Distance Metric for Generative Design Exploration of Diverse Shapes

Khan

Günpinar

Moriguchi

et al. 2019

Journal of Mechanical Design

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In this paper, a generative design approach is proposed that involves the users’ psychological aspect in the design space exploration stage to create distinct design alternatives. Users’ perceptual judgment about designs is extracted as a psycho-physical distance metric, which is then integrated into the design exploration step to generate design alternatives for the parametric computer-aided design (CAD) shapes. To do this, a CAD model is first parametrized by defining geometric parameters and determining ranges of these parameters. Initial design alternatives for the CAD model are generated using Euclidean distance-based sampling teaching–learning-based optimization (S-TLBO), which is recently proposed and can sample N space-filling design alternatives in the design space. Similar designs are then clustered, and a user study is conducted to capture the subjects’ perceptual response for the dissimilarities between the cluster pairs. In addition, a furthest-point-sorting technique is introduced to equalize the number of designs in the clusters, which are being compared by the subjects in the user study. Afterward, nonlinear regression analyses are carried out to construct a mathematical correlation between the subjects’ perceptual response and geometric parameters in the form of a psycho-physical distance metric. Finally, a psycho-physical distance metric obtained is utilized to explore distinct design alternatives for the CAD model. Another user study is designed to compare the diversification between the designs when the Euclidean and the suggested psycho-physical distance metrics are utilized. According to the user study, designs generated with the latter metric are more distinct.

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A Generative Design and Drag Coefficient Prediction System for Sedan Car Side Silhouettes based on Computational Fluid Dynamics

Cited by 52 publications

References 28 publications

Parametric Design and Kansei Engineering in Goblet Styling Design

Parametric Design and Kansei Engineering in Goblet Styling Design

Progress and Challenges in Generative Product Design: A Review of Systems

Evolving a Psycho-Physical Distance Metric for Generative Design Exploration of Diverse Shapes

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