The cognitive challenges in the design of complex engineered systems include the scale and scope of decision problems, nonlinearity of the trade space, subjectivity of the problem formulation, and the need for rapid decision making. These challenges have motivated an active area of research in design decision-support methods and the development of commercial and openly available design frameworks. Although these frameworks are extremely capable, most are limiting as a basis for research relating to design decision support because they offer little user flexibility for incorporating and evaluating new features or techniques. This paper describes Rave (www.rave.gatech.edu), a computational framework designed specifically as a research platform for design decision-support methods. Rave has been structured to be flexible and adaptable, handle data with systematic data structures and descriptive metadata, facilitate a wide spectrum of visualization types, provide features to enable user interactivity and linking of graphics, and incorporate surrogate modeling and optimization as enabling capabilities. This framework is envisioned to provide the research and industrial communities an easily expandable and customizable baseline capability to facilitate investigation of further design decision-support advancements.
Software tools that enable interactive data visualization are now commonly available for engineering design. These tools allow engineers to inspect, filter, and select promising alternatives from large multivariate design spaces based upon an examination of the tradeoffs between multiple objectives. There are two general approaches for visually representing data: (1) discretely, by plotting a sample of designs as distinct points; and (2) continuously, by plotting the functional relationships between design variables and design metrics as curves or surfaces. In this paper, we examine these two approaches through a human subjects experiment. Participants were asked to complete two design tasks with an interactive visualization tool: one by using a sample of discrete designs and one by using a continuous representation of the design space. Metrics describing the optimality of the design outcomes, the usage of different graphics, and the task workload were quantified by mouse tracking, user process descriptions, and analysis of the selected designs. The results indicate that users had more difficultly in selecting multiobjective optimal designs with common continuous graphics than with discrete graphics. The findings suggest that innovative features and additional usability studies are required in order for continuous trade space visualization tools to achieve their full potential.
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