One important source of variance in the performance and success of products designed for use by people is the people themselves. In many cases, the acceptability of the design is affected more by the variance in the human users than by the variance attributable to the hardware from which the product is constructed. Consequently, optimization of products used by people may benefit from consideration of human variance through robust design methodologies. The authors propose that design under uncertainty methodologies can be utilized to generate designs that are robust to variance among users, including differences in age, physical size, strength, and cognitive capability. Including human variance as an inherent part of the product optimization process will improve the overall performance of the product (be it comfort, maintainability, cognitive performance, or other metrics of interest) and could lead to products that are more accessible to broaderpopulations, less expensive, and safer. A case study involving the layout of the interior of a heavy truck cab is presented, focusing on simultaneous placement of the seat and steering wheel adjustment ranges. Tradeoffs between adjustability/cost, driver accommodation, and safety are explored under this paradigm. The current work differs in important ways from standard industry practice for truck design. Unlike typical computer manikin approaches, the stochastic posturing methods explicitly consider residual variance in posture that is unrelated to body dimensions and cab geometry. This allows more accurate quantification of population accommodation and design fitness. The new method is explicitly multivariate and allows simultaneous considerations of multiple design features. Moreover, the current implementation spans a larger range of potential design variables than current tools provided by the Society of Automotive Engineers and can be readily expanded to encompass more. Finally, the new methods allow unambiguous inclusion of both subjective (e.g., comfort) and objective (e.g., safety) metrics in cab optimization. The cab optimization problem is a specific case of the more general problem of designing for human variability. Most approaches to including human variability focus on body dimensions but ignore behavior. The methodology outlined in this paper separately models these two sources of variability in outcomes and adds variability in subjective perception. The application of these techniques requires the collection of data describing the outcomes of interest (driver posture and preference, in the current case) and the development of appropriate statistical models. However, the accuracy and utility of this optimization approach is limited by the validity of the underlying models.


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    Title :

    Robust truck cabin layout optimization using advanced driver variance models


    Additional title:

    Robuste Auslegungsoptimierung einer Fahrerkabine unter Berücksichtigung von Unsicherheiten bezüglich der Körperhaltung des Fahrers




    Publication date :

    2005


    Size :

    7 Seiten, 6 Bilder, 1 Tabelle, 18 Quellen



    Type of media :

    Conference paper


    Type of material :

    Print


    Language :

    English




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