An Experimental Study of Continuous and Discrete Visualization Paradigms for Interactive Trade Space Exploration

German, Brian; Feigh, Karen M.; Daskilewicz, Matthew J.

doi:10.1115/1.4023857

Cited by 13 publications

(5 citation statements)

References 23 publications

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“…Within the design metrics, both included two engineering based design metrics and four economic based design metrics. While a more thorough description of the design tasks can be found in German et al 17 , an outline of the different variables involved in each task is provided in Table 1. The experiment was conducted using 2 PC with Intel Core i7 8=920@1.67 GHz processors, 4GB of RAM, NVIDIA GeForce9800 GTX+ graphics cards and 30" monitors set to a resolution of 2560x1600.…”

Section: Methodsmentioning

confidence: 99%

Impact of Visualization Tools on Design Decision Making

Sullivan

Feigh

German

2012

12th AIAA Aviation Technology, Integration, and Operations (ATIO) Conference and 14th AIAA/ISSMO Multidisciplinary Analysis And

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This report investigates the use of design decision support software by engineering design students, who completed two unfamiliar design tasks using different visualization schemes in a design decision support tool. A mixed methods analysis is used to compare the ability to optimize on multiple dimensions using discrete and continuous design decision support visualization schemes. The analysis considered the qualitative data that was self-reported by the participants in conjunction with the qualitative performance data collected by the researchers to determine the ability of the tool to support design decision making. Results suggest that contrary to self-reported familiarity with both types of visualization schemes, participants were much more likely to optimize on multiple different criteria when using discrete visualization tools than when continuous tools were used. These results, which are supported by several different pieces of qualitative data, provide insight on design decision support tools and help to provide recommendations for future tools. I. Introductionhe engineering design process can be described as a problem in decision making that consists of four main steps: generating alternative designs, evaluating the different alternatives, ranking the alternatives based on the utility of each, and selecting the most highly ranked alternative for development. For most engineering product designs, this process is complex and non-trivial. While the design process can be conducted by optimizing based on a single function or value, it is common to design large-scale engineering systems through a formal systems engineering process, which takes into account multiple requirements and objectives 1 . This method allows engineers to optimize one or more metrics simultaneously, thereby attempting to achieve a balance of economic, performance, and other metrics. Furthermore, engineers use trade studies to search the design space for the best alternative, which is then presented to stakeholders and other engineers. A trade study is a technique of systematically generating alternative designs, evaluating metrics of interest for each alternative, and deducing how the metrics trend with the design variables and with each other. The final decision process that follows the trade study is aided by anecdotal evidence and historic statistical data 2 . The results of engineering trade studies are commonly depicted visually to designers in the form of familiar engineering graphics such as line, carpet, or contour plots 3, 4 . While traditional trade studies produce graphics showing the effect of varying one or two design parameters on a single metric of interest, advances in computing provide new ways to organize engineering analyses and visualize the results. Whereas past plots were created by a computer or by hand and were static, plots created by computers today can be dynamic and updated in real time by engineers, allowing the users to more fully explore the results and comprehend the design problem.

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Section: Methodsmentioning

confidence: 99%

Impact of Visualization Tools on Design Decision Making

Sullivan

Feigh

German

2012

12th AIAA Aviation Technology, Integration, and Operations (ATIO) Conference and 14th AIAA/ISSMO Multidisciplinary Analysis And

View full text Add to dashboard Cite

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“…Past engineering design interfaces have been constructed to address the challenges of complex systems design, especially the need to consider multiple objectives in searches with visualizations (Zhang et al 2012;Woodruff et al 2013) and trade-offs in multi-dimensional and multivariate data (German et al 2013). Progress in effective GUI approaches is relevant to design because engineers must be capable of effectively manipulating a system and its components toward a desirable goal state-an effective GUI should enable the engineer to learn essential aspects of complex systems that aid in design.…”

Section: Software Interfaces For Engineering Designmentioning

confidence: 99%

“…The investigation is informed by recent cognitive and engineering studies that demonstrate the usefulness of software tools including multi-level graphical user interfaces (GUIs) and agent-based renderings in improving human understanding and design of systems (Chi et al 2012;Li & Gunal 2012;Zhang et al 2012;German, Feigh & Daskilewicz 2013;Jordan et al 2013;Woodruff, Reed & Simpson 2013). Specifically, we seek to demonstrate how differently configured GUIs and agentbased simulations can facilitate the learning of inter-level causal mechanisms (Chi 2005) (i.e., how perturbations at one level of organization influence the system behavior at another level) and parametric relationships (Egan et al 2015a), and how this information about inter-level causal mechanisms translates to improved human performance during multi-level design optimization tasks.…”

Section: Introductionmentioning

confidence: 99%

“…2012; Li & Gunal 2012; Zhang et al. 2012; German, Feigh & Daskilewicz 2013; Jordan et al. 2013; Woodruff, Reed & Simpson 2013).…”

Section: Introductionmentioning

confidence: 99%

“…2013) and trade-offs in multi-dimensional and multivariate data (German et al. 2013). Progress in effective GUI approaches is relevant to design because engineers must be capable of effectively manipulating a system and its components toward a desirable goal state—an effective GUI should enable the engineer to learn essential aspects of complex systems that aid in design.…”

Section: Introductionmentioning

confidence: 99%

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Improving human understanding and design of complex multi-level systems with animation and parametric relationship supports

et al. 2015

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(2015). Improving human understanding and design of complex multi-level systems with animation and parametric relationship supports. Design Science, 1, e3 doi:10.1017/dsj. 2015 AbstractComplex 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.

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