Deciphering the Influence of Product Shape on Consumer Judgments Through Geometric Abstraction

Orbay, Gunay; Fu, Luoting; Kara, Levent Burak

doi:10.1115/1.4030206

Cited by 18 publications

(16 citation statements)

References 23 publications

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“…And based on these two, (3) how do we search for a good design? Challenges in answering these questions include high-dimensional or ill-defined design spaces such as for topologies [10,11], material microstructures [12,13,14], or complex geometries [15,16], expensive evaluations of designs and their sensitivities, e.g., due to model nonlinearity [17,18], coupled materials or physics [19,20,21], or subjective goodness measures [22,23], or search inefficiency due to the absence of sensitivities [24,25,26] or the existence of random variables [27].…”

Section: Related Workmentioning

confidence: 99%

One-shot generation of near-optimal topology through theory-driven machine learning

Cang

Yao

Ren

2019

Computer-Aided Design

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We introduce a theory-driven mechanism for learning a neural network model that performs generative topology design in one shot given a problem setting, circumventing the conventional iterative process that computational design tasks usually entail. The proposed mechanism can lead to machines that quickly response to new design requirements based on its knowledge accumulated through past experiences of design generation. Achieving such a mechanism through supervised learning would require an impractically large amount of problem-solution pairs for training, due to the known limitation of deep neural networks in knowledge generalization. To this end, we introduce an interaction between a student (the neural network) and a teacher (the optimality conditions underlying topology optimization): The student learns from existing data and is tested on unseen problems. Deviation of the student's solutions from the optimality conditions is quantified, and used for choosing new data points to learn from. We call this learning mechanism "theory-driven", as it explicitly uses domain-specific theories to guide the learning, thus distinguishing itself from purely data-driven supervised learning. We show through a compliance minimization problem that the proposed learning mechanism leads to topology generation with near-optimal structural compliance, much improved from standard supervised learning under the same computational budget. example, the design of vehicle body-in-white is often done by experienced structure engineers, since topology optimization (TO) on full-scale crash simulation is not yet fast enough to respond to requests from higher-level design tasks, e.g., geometry design with style and aerodynamic considerations, and thus may slow down the entire design process 1 .Research exists in developing deep neural network models that learn to create structured solutions in a one-shot fashion, circumventing the need of iterations (e.g., in solving systems of equations [1], simulating dynamical systems [2], or searching for optimal solutions [3,4,5]). Learning of such models through data, however, is often criticized to have limited generalization capability, especially when highly nonlinear input-output relations or highdimensional output spaces exist [6,7,8]. In the context of TO, this means that the network may create structures with unreasonably poor physical properties when it responds to new problem settings. More concretely, consider a topology with a tiny crack in one of its trusses. This design would be far from optimal if the goal is to lower compliance, yet standard datadriven learning mechanisms do not prevent this from happening, i.e., they don't know that they don't know (physics).Our goal is to create a learning mechanism that knows what it does not know, and thus can self-improve in an effective way. Specifically, we are curious about how physicsbased knowledge, e.g., in the forms of dynamical models, theoretical bounds, and optimality conditions, can be directly injected into the learning of networks that ...

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Section: Related Workmentioning

confidence: 99%

One-shot generation of near-optimal topology through theory-driven machine learning

Cang

Yao

Ren

2019

Computer-Aided Design

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“…The stereotype they identified for each group had a distinct body shape and feature designs. Orbay et al [18] tested consumer judgments of cars' abstraction models and full models. They observed that subjects associated body shapes with certain unobservable attributes.…”

Section: Effects Of Body Shape and Features On Consumermentioning

confidence: 99%

A Test of the Rapid Formation of Design Cues for Product Body Shapes and Features

MacDonald

2018

Journal of Mechanical Design

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Consumers often use a product's visual design as a mental shortcut to judge its unobservable attributes. Mental associations between visual design and unobservable attributes aid consumers in their judgments, and hypothetically reduce consumers' mental load. This paper describes a study that shows the possibility of quickly creating an association in subjects' minds between a holistic visual cue of a product—its body shape—and the general idea of “environmentally friendly” versus “not environmentally friendly,” a typically unobservable attribute. In this study, products' actual environmental friendliness was not measured. Subjects completed an association-building task, in which they developed mental associations between a product's visual cues and its “environmental friendliness” rating, an arbitrarily predetermined rating the authors supplied. The body shape was successfully used as a cue to subliminally communicate to subjects the product's “environmental friendliness.” As a comparison, an individual feature of the product was also used to cue; however, that was unsuccessful. An eye-tracking device was used to identify where subjects were focusing their eyes and for how long. In both the association-building task and a testing task that followed, subjects spent a greater percentage of time looking at the product's cued areas (the body and the selected feature). But during the testing task, subjects spent an even higher percentage of their time looking at the cued areas than they did during the association-building task. This indicates that mental associations, or cues, work to distribute mental load more efficiently.

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“…Recent studies have shown that brand recognition is dependent on the fidelity of the design representation (Ranscombe et al 2012;Rasoulifar, Prudhomme & Eckert 2015). Informally, there is a level of realism to the design that must be achieved for customers to correctly identify vehicle brand (Orbay, Fu & Kara 2015). We build on this notion by representing vehicle designs using the highest fidelity representation possible whether a 2D image or a 3D high polygon mesh, as shown in Figures 1 and 2, respectively.…”

Section: D and 3d Representationsmentioning

confidence: 99%

Balancing design freedom and brand recognition in the evolution of automotive brand styling

et al. 2016

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Designers faced with the task of developing a new product model of a brand must balance several considerations. The design must be novel and express attributes important to the customers, while also recognizable as a representative of the brand. This balancing is left to the intuition of the designers, who must anticipate how customers will perceive the new design. Oftentimes, the design freedom used to meet a product attribute can compromise the recognition of the product as a member of the brand. In this paper, an experiment is conducted for measuring changes in ten styling attributes common to both design freedom and brand recognition for automotive designs from four brands, Audi, BMW, Cadillac, and Lexus, using customer responses to two- and three-dimensional vehicle designs created and presented interactively through a crowdsourced web application. Results show that while brand recognition is highly dependent on the manufacturer, two brands have strong negative relationship between design freedom and brand recognition, suggesting that these two manufacturers face a significant challenge when evolving their respective brand styling. This study is a first effort toward quantifying and predicting tradeoffs between design freedom and brand recognition, contributing to existing efforts that augment human intuition during strategic design decisions.

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Deciphering the Influence of Product Shape on Consumer Judgments Through Geometric Abstraction

Cited by 18 publications

References 23 publications

One-shot generation of near-optimal topology through theory-driven machine learning

One-shot generation of near-optimal topology through theory-driven machine learning

A Test of the Rapid Formation of Design Cues for Product Body Shapes and Features

Balancing design freedom and brand recognition in the evolution of automotive brand styling

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