The ability to effectively analyse design concepts is essential for making early stage design decisions. Human evaluations, the most common assessment method, describe individual design concepts on a variety of ideation metrics. However, this approach falls short in creating a holistic representation of the design space as a whole that informs the underlying relations between concepts. Motivated by this shortcoming, this work leverages network theory to visualize and characterize features of a conceptual design space. To illustrate the utility of network theory for these purposes, a network composed of a corpus of solutions to a design problem and their semantic similarity is derived, and its design properties (e.g., uniqueness and innovation potential) are studied. This network-based approach not only characterizes features of individual designs themselves, but also uncovers more nuanced properties of the design space through studying emerging clusters of concepts. Overall, this work expands on developing research in design, demonstrating the value in applying network analytics to a conceptual design space as an engineering support tool to aid design decision-making.

The use of grammars in design and analysis has been set back by the lack of automated ways to induce them from arbitrarily structured datasets. Machine translation methods provide a construct for inducing grammars from coded data which have been extended to be used for design through pre-coded design data. This work introduces a four-step process for inducing grammars from un-coded structured datasets which can constitute a wide variety of data types, including many used in the design. The method includes: (1) extracting objects from the data, (2) forming structures from objects, (3) expanding structures into rules based on frequency, and (4) finding rule similarities that lead to consolidation or abstraction. To evaluate this method, grammars are induced from generated data, architectural layouts and three-dimensional design models to demonstrate that this method offers usable grammars automatically which are functionally similar to grammars produced by hand.

The design process often requires work by teams, rather than individuals. During team based design it is likely that situations will arise in which individual members of the team have different opinions, yet a group decision must still be made. Unfortunately, Arrow’s impossibility theorem indicates that there is no method for aggregating group preferences that will always satisfy a small number of ‘fair’ conditions. This work seeks to identify methods of combining individual preferences that can come close to satisfying Arrow’s conditions, enabling decisions that are fairer in practice. First, experiential conjoint analysis was used to obtain individual empirical utility functions for drinking mug designs. Each empirical utility function represented individual members who were part of a design team. Then, a number of functions for constructing group preference were analysed using both randomly generated preferences and empirical preferences derived from the experiential conjoint survey. The analysis involved checking each of Arrow’s conditions, as well as assessing the potential impact of strategic voting. Based on the results, methods that should be used to aggregate group preference within a design team in practice were identified and recommended.

Complex systems are challenging to design, particularly when they contain multi-level organizations with non-obvious relationships among design components. Here, we investigate engineering students’ capacity to search for optimal nanoscale biosystem designs with stochastic component and system behaviors. The study aims to characterize information types that facilitate human learning and improve their complex system understanding and design proficiency. It is hypothesized that learning parametric system relationships and/or inter-level causal mechanisms improves design proficiency; these relationships and mechanisms are teachable through software interfaces. Two contrasting learning/design interfaces were developed that presented differing information types: an interface with performance charts that emphasized parametric relationship learning and an interface with agent-based animations that emphasized inter-level causality learning. Users improved on pre-/post-learning design tasks with both interfaces; users who demonstrated inter-level causal relationship understanding, which occurred primarily with the animation interface, had greater improvement. All users were then presented contrasting animations of systems with opposing emergent behaviors, resulting in many more participants demonstrating an understanding of inter-level causal behaviors. These findings reveal the difficulties in understanding and designing multi-level systems and that interactive software tools may convey crucial information that supports engineering design, particularly with respect to the development of reasoning skills for how system components relate across levels.

Over the past 20 years I have focused on the synthesis process, the process of creation, of bringing together ideas and objects to fulfill and enable needs. Synthesis is a core engineering activity, the partner in the design and analysis cycle of engineering (and other) design. Synthesis is the basis of innovation, the enabler of creating that which is new. Over the past 20 years, research and practice in the area of synthesis and innovation has become more intricate, more complex, and more complete in its breadth of exploration and depth of understanding and delivery. Research in the area that was started over the past 10 to 20 years is now being commercialized, beginning to impact the way design is practiced. The state of the art of algorithms, theories, and processes for the computing basis of synthesis research today can be found in Formal Engineering Design Synthesis (Antonsson & Cagan, 2001). This discussion is not a review of the literature or state of the art, but rather my views of the field of innovation, its emergence into a scientific study, areas of focus for future research, and some of my experiences in each of these areas.

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.

Organizational research has shown that effectively structuring the resources (human, informational, computational) available to an organization can significantly improve its collective computational capacity. Central to this improved capacity is the manner in which the organization's member agents are related. This study is an initial investigation into the role and potential of interagent ties in computational teaming. A computational team-based model, designed to more fully integrate agent ties, is created and presented. It is applied to a bulk manufacturing process-planning problem and its performance compared against a previously tested agent-based algorithm without these agent relationships. The performance of the new agent method showed significant improvement over the previous method: improving solution quality 280% and increasing solution identification per unit time an entire order of magnitude. A statistical examination of the new algorithm confirms that agent interdependencies are the strongest and most consistent performance effects leading to the observed improvements. This study illustrates that the interagent ties associated with team collaboration can be a highly effective method of improving computational design performance, and the results are promising indications that the application of organization constructs within a computational context may significantly improve computational problem solving.

A framework for a design tool based on shape grammars is presented as an effective means for supporting the early stages of design. The framework uses a shape grammar interpreter to implement parametric shape grammars, allowing the grammar to be used interactively by a designer or optimization routine. A shape grammar to design inner hood panels of vehicles is introduced as an example of a parametric engineering shape grammar, and it is used with the framework to create standard and novel designs made possible by rules that take advantage of shape emergence.

Production systems have traditionally been used in engineering to aid the designer in generating designs in domains not significantly influenced by geometry. However, in geometric-based domains such as mechanical engineering, their popularity has been limited. This work proposes the use of shape grammars as the framework for engineering expert systems. Shape grammars are shown to have all the generation and analysis capabilities of traditional production systems, while being able to represent knowledge about both the functionality and the form of a product. Additionally, the parametric nature of shape grammars, their ability to deal with physical form rather than abstract elements, and their ability to recognize emergent shapes give them significant advantages over traditional production systems for geometry-based engineering design.

Applying grammatical formalisms to engineering problems requires consideration of spatial, functional, and behavioral design attributes. This paper explores structural design languages and semantics for the generation of feasible and purposeful discrete structures. In an application of shape annealing, a combination of grammatical design generation and search, to the generation of discrete structures, rule syntax, and semantics are used to model desired relations between structural form and function as well as control design generation. Explicit domain knowledge is placed within the grammar through rule and syntax formulation, resulting in the generation of only forms that make functional sense and adhere to preferred visual styles. Design interpretation, or semantics, is then used to select forms that meet functional and visual goals. The distinction between syntax used in grammar rules to explicitly drive geometric design and semantics used in design interpretation to implicitly guide geometric form is shown. Overall, the designs presented show the validity of applying a grammatical formalism to an engineering design problem and illustrate a range of possibilities for modeling functional and visual design criteria.

A grammatical approach to product design is demonstrated. In particular, shape grammars are shown to be especially useful for products that are differentiated primarily on the basis of form yet driven by function; they allow products to be designed as a sequence of well-defined steps. However, it is not always clear how to choose the sequence of rules that should be applied to generate the final shape. In this paper we demonstrate that at each stage during the process, partial designs of the final product can be used to provide feedback to the designer based on specific design objectives and thus suggest possible rule choices. We take advantage of the shape grammar for the generation of coffeemakers introduced by Agarwal and Cagan, and associate with the grammar rules expressions that model manufacturing costs. With each application of a shape grammar rule, an understanding of the overall cost of manufacturing the product is incrementally improved. Thus, at each stage of the design process the designer has an indication of what the overall cost of the product will be and how the selection of one grammar rule over another influences the final cost. Once the complete product is generated, an appraisal of its manufacturing cost is given to the designer. This evaluation methodology helps the designer understand the implications of decisions made early on in the design process. We have also verified the accuracy of this approach through the costs of some commercially available coffeemakers, generated by this method, which are comparable to the costs for those designs listed in the literature.