Deconstruction of Idea Generation Methods Into a Framework of Creativity Mechanisms

Creative ideas are a central part of solving engineering problems, generating interesting art, as well as developing successful products and innovations. Idea generation methods are a well-researched topic. Specifically, there is significant research that focuses on specific idea generation methods and how they perform. Further, some method classifications have been suggested to help understand the cognitive mechanisms involved in creative ideation as well as the differences between methods. Yet, the discourse is usually on which ideation method outperforms another or how to improve an ideation method rather than the elements, rules, constraints, and activities that comprise ideation methods. In this study 76 well-documented idea generation methods are reviewed and analyzed. We find all analyzed methods consist of 25 mechanisms. The mechanisms are discussed and classified into idea promoting and implementation mechanisms. We suggest that rather than focusing research only on methods, there should be a parallel track of research creating understanding on these mechanisms and their interactions to help increase our understanding of creativity methods, add practitioners understanding on how to get the best advantages out of creativity methods and lastly improve the way practical creativity is approached in education.

Early-Stage Uncertainty: Effects of Robust Convex Optimization on Design Exploration

Engineers design for an inherently uncertain world. In the early stages of design processes, they commonly account for such uncertainty either by manually choosing a specific worst-case and multiplying uncertain parameters with safety factors or by using Monte Carlo simulations to estimate the probabilistic boundaries in which their design is feasible. The safety factors of this first practice are determined by industry and organizational standards, providing a limited account of uncertainty; the second practice is time intensive, requiring the development of separate testing infrastructure. In theory, robust optimization provides an alternative, allowing set based conceptualizations of uncertainty to be represented during model development as optimizable design parameters. How these theoretical benefits translate to design practice has not previously been studied. In this work, we analyzed present use of geometric programs as design models in the aerospace industry to determine the current state-of-the-art, then conducted a human-subjects experiment to investigate how various mathematical representations of uncertainty affect design space exploration. We found that robust optimization led to far more efficient explorations of possible designs with only small differences in an experimental participant’s understanding of their model. Specifically, the Pareto frontier of a typical participant using robust optimization left less performance “on the table” across various levels of risk than the very best frontiers of participants using industry-standard practices.

Only As Strong As the Strongest Link: The Impact of Individual Team Member Proficiency in Configuration Design

Prior research has demonstrated how the average characteristics of a team impact team performance. Individual characteristics of team members and individual team member behavior have been largely ignored, especially in the context of engineering design. In this work, a behavioral study was conducted to uncover whether the most or least proficient member of a configuration design team had a larger impact on overall performance. It was found that a configuration design team is most dependent on the proficiency of its most proficient member and results suggest that replacing the most proficient member with an even more proficient member can be expected to have a more positive impact than replacing any other member with a higher proficiency member of the same change in proficiency. The most proficient member had a significant positive effect on how quickly the team reached performance thresholds and that the other members of the team were not found to have the same positive impact throughout the design study. Behavioral heuristics were found using hidden Markov modeling to capture the differences in behavior and design strategy between different proficiency members. Results show that high proficiency and low proficiency team members exhibit different behavior, with the most proficient member’s behavior leading to topologically simpler designs and other members adopting their designs, leading to the most proficient member driving the team design and team performance.

The Value of Prototyping: An Investigation of the Relationship Between the Costs of Prototyping, Perceived Value, and Design Outcome

Prototyping is an important part of the design process and has repeatedly been identified in prior work as an important tool for designers to test assumptions, communicate ideas, and develop design knowledge. Researchers, however, currently have a limited understanding of how the resources invested in a prototype influence designers’ decision-making and their perceptions of a prototype’s value. Prior work has shown that significant investment of time or money in a prototype can lead to undesirable effects such as design fixation, but the full impact of these factors on designers’ perceived value of the prototypes remains unclear. Likewise, it is unclear how prototype usage impacts the evolution of designer knowledge. To explore these relationships, a study was performed in a 16 week-long design project involving 32 teams of mechanical engineering students. Results suggest that effective prototyping uncovered new design knowledge and limited uncertainty early in the design process, allowing teams to spend more time testing and iterating later in the design process. High-performing teams also reported final prototypes as less valuable for gathering new knowledge than their peers. Importantly, the study did not find any significant relationships between the cost of a prototype in terms of money and time, and the perceived value of that prototype. Nor were any significant relationships found between costs and final design outcomes. This work underscores the need for better methods to evaluate the value of prototyping efforts.

A Pre-Prototyping Framework to Explore Human-Centered Prototyping Strategies During Early Design

Current prototyping frameworks are often prompt-based and heavily rely on designers’ experience. The lack of systematic guidelines in prototyping activities causes unwanted variation in the quality of the prototype. Notably, there is limited, or no prototyping framework exists that enables human factors engineering (HFE) guidelines be part of the early product development process. In this paper, a pre-prototyping framework is proposed to render human-centered design strategies to guide designers before the hands-on prototyping activity starts. The methodology consists of extracting key factors related to prototyping and human factors engineering principles based on an extensive literature review. The key elements are then combined to form the prototyping categories, dimensions (theory), and tools (practice). The resulting prototyping framework can be used to develop prototyping strategies consist of theoretical guidelines and practical tools that are needed during the prototyping of human-centered products. The framework provides systematic guidance to designers in the early stages of the design process so that designers, in particular novices in ergonomics and human factors, can have a head start in building the prototypes in the right direction. Finally, a case study is presented to demonstrate a walk-through and efficacy of the proposed pre-prototyping framework.

A Comparison of Vector and Network-Based Measures for Assessing Design Similarity

In order to retrieve analogous designs for design-by-analogy, computational systems require the calculation of similarity between the target design and a repository of source designs. Representing designs as functional abstractions can support designers in practicing design-by-analogy by minimizing fixation on surface-level similarities. In addition, when a design is represented by a functional model using a function-flow format, many measures are available to determine functional similarity. In most current function-based design-by-analogy systems, the functions are represented as vectors and measures like cosine similarity are used to retrieve analogous designs. However, it is hypothesized that changing the similarity measure can significantly change the examples that are retrieved. In this paper, several similarity measures are empirically tested across a set of functional models of energy harvesting products. In addition, the paper explores representing the functional models as networks to find functionally similar designs using graph similarity measures. Surprisingly, the types of designs that are considered similar by vector-based and one of the graph similarity measures are found to vary significantly. Even among a set of functional models that share known similar technology, the different measures find inconsistent degrees of similarity — some measures find the set of models to be very similar and some find them to be very dissimilar. The findings have implications on the choice of similarity metric and its effect on finding analogous designs that, in this case, have similar pairs of functions and flows in their functional models. Since literature has shown that the types of designs presented can impact their effectiveness in aiding the design process, this work intends to spur further consideration of the impact of using different similarity measures when assessing design similarity computationally.

Effects of Locus of Control Personality Trait on Team Performance in Cooperative Engineering Design Tasks

Teams in engineering design tackle problems that exceed the abilities of individuals. Improved understanding of how personality traits influence human behaviors and interaction may help create new methods and tools to support design teams. This paper seeks to understand how the Locus of Control (LOC) personality trait influences designer behaviors and team performance. A designer experiment studies 12 participant pairs controlled for categorical LOC pairing factors (internal-internal, external-external, and internal-external). Each design team completes six simplified cooperative parameter design tasks to minimize completion time, yielding 72 total data points. Regression analysis shows LOC pairing affects team efficiency in agreement with literature outside engineering design: diverse LOC traits reduce design efficiency while similarity increases team effectiveness. Results contribute to an explanatory hypothesis that LOC pairing influences designer behaviors related to action effectiveness which, subsequently, affects team performance outcomes.

Development of Spatial Abilities in Engineering Education: An Empirical Study of the Influence of Visualisation Media

Spatial ability is one of the critical components of human intelligence. It has been proven that it is particularly crucial for success, especially in engineering, where interpreting views of an object presented by drawings, visualizing parts, or manipulating geometry in CAD are fundamental skills. Research has confirmed that spatial skills can be improved through instruction and teaching, for example, sketching and technical drawing, which are also included in the basic engineering classes. This study tested the hypothesis that the development of spatial skills during the fundamental design engineering class can be positively influenced due to the use of different visualization media for sketching and technical drawing tasks, whereas the used visualization media offer varying possibilities of interaction. Seventy students were pre- and post-tested with the Mental Rotation Test. All participants received the same training during the class but were given individual tasks with varying media. The analysis revealed a significant increase in mental rotation performance for all participants throughout the semester with a big effect size. Moreover, the mean performance improvement differed considerably depending on the visualization media and its offered interaction possibilities.

Paired Computer-Aided Design: The Effect of Collaboration Mode on Differences in Model Quality

We present the results of an experiment investigating two different modes of collaboration on a series of computer-aided design (CAD) tasks. Inspired by the pair programming literature, we anticipate that partners working in a fully synchronous collaborative CAD environment will achieve different levels of quality in CAD models depending on their mode of collaboration — one in which the pair is free to work in parallel, and another where the pair must coordinate to share one control. We found that a shared CAD control led to significantly better overall CAD quality than parallel CAD control. In addition, the shared control mode led to more complete and consistent CAD models, as well as the tendency for participants to follow instructions to correctly replicate features for the design task. As is predicted in the literature, a trade-off relationship (albeit weak) between quality and speed via the parallel collaboration was found. In contrast, the shared control mode shows no clear relationship between speed and quality. Collaborative CAD is increasingly seen as an appealing tool for modern product design teams. This study suggests that the benefits of this tool are not solely the effect of the tool itself, but result from the collaboration style of the designers using the tool.

Measuring Human Learning in Design Space Exploration to Assess Effectiveness of Knowledge Discovery Tools

While there exist various knowledge discovery tools to support designers’ learning during design space exploration, there is no established definition of what is expected to be learned and how it should be measured. Measuring learning is important, as it enables assessing and comparing different knowledge discovery methods. In this paper, we review the major categories of learning goals that are introduced in the field of education. Then, 7 different measures are developed to target specific learning goals relevant to design space exploration. Different learning goals are targeted by modifying the domain of the knowledge that is tested by each measure, and the specific task that the user is asked to perform. A human-subject experiment is conducted to measure how these metrics are related. Specifically, the consistency and correlations between different combinations of the measures are examined. Based on the observations made in this study, we discuss the implications and issues for future usage.