Visualization of manufacturing process data in N-dimensional spaces: a reanalysis of the data

As process engineers are forced to better understand and control their manufacturing processes, the ability to identify the relationships between variables becomes important to the control of the processes. There are various methods available to facilitate the identification of quantitative relationships, but all are somewhat limited in their ability to characterize important relationships involving large numbers of variables and vast amounts of data. A previous study compared the ability of subjects to identify relationships on the basis of several different modes of displaying process data. The study also explored options for data visualization techniques that would aid the identification of complex relationships by combining data display capabilities of high resolution graphic work stations and the pattern recognition capabilities of humans. Results showed that subjects who could accurately use a three-dimensional display had faster response times than subjects who used a two-dimensional display. This paper discusses a more in depth analysis of the data from the previous study. Specifically, it examines the influences of data visualization style, perceptual complexity, and informational complexity on the user's response times and accuracy. The reanalysis confirms and further explains earlier findings. That is, three-dimensional displays improve performance when information displayed is perceptually complex, whereas informational complexity is best displayed in two dimensions. The applicability of this research to process characterization, statistical analyses, and other software tools is discussed.