Creating Cross-Over Vehicles: Defining and Combining Vehicle Classes using Shape Grammars

In the push for new vehicle designs, the distinctions between vehicle classes are quickly becoming blurred. We use shape grammars to quantify the differences between vehicle classes through the application of class-specific rules and the constraint of rule applications to within parametric ranges determined for each class. This allows for the development of new vehicle forms that clearly fall within a class or purposefully cross over the boundaries of classes and mix rules and ranges to create unique and interesting cross-over vehicles

Quantifying Aesthetic Preference Through Statistics Applied to an Agent-Based Shape Grammar Implementation

In the development of a new product, a large amount of consideration is given to the outward form. Because this is the first part that a consumer comes into contact with, the impression it gives and what it communicates can determine all future interactions from purchase to disposal. Historically, designers must rely upon focus groups, customer feedback, experience, and intuition or gut to design products that the public will find attractive. A shape grammar offers the potential to quantify what consumers prefer aesthetically. The shape grammar can be implemented in an agent-based program that allows a person\u27s preferences to be tracked, quantified, and then summarized in a utility function based on statistical groupings of elements. The agent-based program can implement the shape grammar to generate new product concepts that match the derived utility function. This utility function can also be employed by designers to ensure that the concepts they are developing target consumer preferences

Multiagent Shape Grammar Implementation: Automatically Generating Form Concepts According to a Preference Function

In new product development, quickly generating many product form concepts that a potential consumer prefers is a challenge. This paper presents the inaugural multiagent shape grammar implementation (MASGI) to automatically generate product form designs according to a preference function that can represent designer or consumer design preference. Additionally, the multiagent system creates a flexible shape grammar implementation that enables modifications to the shape grammar as the form design space changes. The method is composed of three subprocesses: a shape grammar interpreter that implements the shape grammar, an agent system that chooses which shape grammar rules to implement and the parametric design choices according to a preference function, and a preference investigator that determines the preference function, which constraints the automated form design process

Automatically Generating Form Concepts According to Consumer Preference: A Shape Grammar Implementation with Software Agents

In new product development, quickly generating many concepts that a potential consumer prefers is a challenge. This paper presents the inaugural application of software agents implementing a shape grammar to generate product designs according to a utility function that represents consumer preference. The method is composed of three sub-processes: a shape grammar interpreter, an agent interpreter, and a utility investigator. These work together to explore the design space and can constrain product form designs according to a utility function that represents consumer design preference

Capturing Interactions in Design Preferences: A Colorful Study

Many engineering and marketing tools exist to help a designer optimize quantitative attributes of a product, such as height, weight, volume, or cost. However, these methods cannot effectively take into consideration attributes for which there is a significant interaction between the product attributes with respect to the consumer\u27s preference, such as aesthetics. This research has begun the work of developing this necessary functional relationship for product attribute interactions and has created a methodology for further research. To accomplish this, this study considered consumer preference for product colors. Colors were represented by their red, green, and blue light components, and preference information for each of these attributes was gathered by presenting individuals with a small sample of colors, applied to backpacks, in a short choice survey

Cued Active Learning: An Initial Study

It has become common knowledge that effective teaching requires more than just the rote dissemination of knowledge. By using active learning, teachers involve the students in the learning process. As the students subjectively handle the class material, their comprehension and retention improves. In the classroom, teachers commonly prompt actively learning through a verbal cue such as, “We are now going to break into groups.” This forces the student to switch from a receptive mental state to an active mental state. We theorize that this verbal, short duration transition from lecture to active learning, especially in large classroom settings, is not sufficient to make this transition quickly and thus limits how active students are in the active learning session. In this paper we present a technique and exploratory study results for cueing active learning through a representative icon in a visual lecture presentation. This cue enables the students to mentally prepare themselves for actively learning during a more passive part of the lecture. The results of our exploratory study demonstrate that the cued active learning did not conclusively correlate with average student performance, but that it did show a decrease in the standard deviation of performance, thereby demonstrating an improvement in the comprehension of the students that were more likely to perform lower than average. The results of this study will be used to conduct a more formal study including direct measurement of lecture participation by students

Automating the Creation of Shape Grammar Rules

Shape grammars have the potential to be used in many design applications. One of the most limiting factors is that, currently, a shape grammar must be created by an expert trained in the creation and usage of shape grammars. In this paper we describe how the rules for a shape grammar can be derived automatically. A statistical analysis of the design language produces fundamental shape chunks. These chunks then form the basis for the shape grammar rules. These rules are created objectively and automatically and fewer rules are needed to create a completed design. Additionally, the form of the rules encourages divergent designs. Unique concept vehicles are given as an example

Quantifying and Maximizing Aesthetic Preference

One of the greatest challenges in new product development is the creating of a product form that is attractive to an intended market audience. Just as choice-based conjoint has been successfully utilized to explore product features, we have developed methods that enable and support conjoint analysis to explore consumer preferences within a continuous parametric range of visual aesthetics (physical product forms). We apply our work to vehicle design, where our application can facilitate vehicle design by providing a time and cost efficient method to obtain market research on aesthetic preferences for vehicle design. In general, this methodology will allow product developers to incorporate rich preference feedback from the market about product form, where market preferences can be collected extremely early in the development of the product concept

A New Approach to Vehicle Concept Generation: A Statistics-Based Method for Creating Innovative Product Forms

When creating vehicle concepts, designers often follow a common methodology. Initial sketches center around the flow of the vehicle, focusing first on curves that sweep from front to back. As the design progresses through the development stages these expressive curves are constrained into specific vehicle characteristics. By looking at vehicles in their final form much can be learned about how to create new vehicle concepts. For example, vehicle characteristics like the hood, bottom of the side windows, and trunk would be grouped together and represented in the initial concept by a belt line that sweeps the whole of the vehicle from front to rear. Experience, training, and human intuition are used by designers to understand how to represent the vehicle characteristics in initial concepts. The initial representation drives the vehicle towards its final form. In this work we introduce a new method for vehicle concept creation based on a statistical analysis of similarities and differences in a vehicle class. Representative chunking of vehicle characteristics is determined through a statistical analysis of existing vehicles, not through human intuition. A sample of existing coupe vehicles is gathered. Each vehicle is decomposed into 22 vehicle characteristics, e.g. headlights. A minimum number of four-control-point Bezier curves that sufficiently capture the form of the characteristic are used. Through principal component analysis the control points, and thereby the curves, that differentiate the most between vehicles are chunked together. These statistically derived curve chunks, often unintuitive and non-obvious, but effective, are used as the foundation for creating new vehicle concepts. A traditional conceptualization methodology limits the designer\u27s exploration of the concept space by introducing curves in the same order. The method discussed here, based upon statistical chunking of curves, encourages the designer to deviate from tradition and thereby explore more of the design space. Additionally, since the curve chunks are based upon their influence on a sample set of designs, the designer is guaranteed to consider the most influential curves and their impact on conceptualization. In the method, the designer chooses a curve chunk and introduces the curves to the concept. Based upon which curves have already been drawn, the designer picks a new chuck and adds more curves to the concept. Thus, a concept flows from chunk to chunk until all the statistically derived curves have been introduced. The designer then fills in the rest of the curves and adds details as seen fit