A product platform is a set of common components, modules or parts from which a stream of derivative products can be created. Product platform design requires selection of the shared parts and assessment of the potential sacrifices in individual product performance that result from parts sharing. A multicriteria optimization problem can be formulated to study such decisions in a quantitative manner at the product performance level. Studying the Pareto sets that correspond to various derivative products leads to a systematic methodology for design decision making. Design of a nail gun platform is used to illustrate the concepts presented. �DOI: 10.1115/1.1355775�

One important source of variability 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 variability in the human users than by the variability attributable to the hardware from which the product is constructed. Designing for human variability as an inherent part of the product optimization process can improve the overall performance of the product. This paper presents a new approach to artifact design that applies population sampling and stochastic posture prediction in an optimization environment to achieve optimal designs that are robust to variability among users, including differences in age, physical size, strength, and cognitive capability. 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. Trade-offs between adjustability (an indicator of cost), driver accommodation, and safety are explored under this paradigm.

The reach capability of drivers is currently represented in vehicle design practice in two ways. The SAE Recommended Practice J287 presents maximum reach capability surfaces for selected percentiles of a generic driving population. Driver reach is also simulated using digital human figure models. In typical applications, a family of figure models that span a large range of the target driver population with respect to body dimensions is positioned within a digital mockup of the driver's workstation. The articulated segments of the figure model are exercised to simulate reaching motions and driver capabilities are calculated from the constraints of the kinematic model. Both of these current methods for representing driver reach are substantially limited. The J287 surfaces are not configurable for population characteristics, do not provide the user with the ability to adjust accommodation percentiles, and do not provide any guidance on the difficulty of reaches that are attainable. The figure model method is strongly dependent on the quality of the models used for posturing and range of motion, and, in any case, cannot reliably generate population distributions of either reach capability or difficulty. A new method of modeling driver reach capability is presented. The method is based on a unified model of reach difficulty and capability in which a maximum reach is a maximally difficult reach. The new approach is made possible by new measurement methods that allow detailed and efficient sampling of an individual's reachdifficulty function. This paper summarizes the experimental approach and presents the structure of the new integrated model of population reach difficulty and capability.

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Nonlinear constrained optimization algorithms are widely utilized in artifact design. Certain algorithms also lend themselves well to design of experiments (DOE). Adaptive design refers to experimental design where determining where to sample next is influenced by information from previous experiments. We present a constrained optimization algorithm known as superEGO (a variant of the EGO algorithm of Schonlau, Welch and Jones) that is able to create adaptive designs effectively. Its ability to allow easily for a variety of sampling criteria and to incorporate constraint information accurately makes it well suited to the needs of adaptive design. The approach is demonstrated on a human reach experiment where the selection of sampling points adapts successfully to the stature and perception of the individual test subject. Results from the initial study indicate that superEGO is able to create experimental designs that yield more accurate models using fewer points than the original testing procedure.

This research demonstrates the relevance of some anthropometry-based ergonomics concepts to the field of design for sustainability. A global design case study leverages human variability considerations in furthering three sustainable design goals: reducing raw material consumption, increasing usage lifetimes and incorporating ethical human resource considerations in design.

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Anthropometric data are widely used in the design of chairs, seats, and other furniture intended for seated use. These data are valuable for determining the overall height, width, and depth of a chair, but contain little information about body shape that can be used to choose appropriate contours for backrests. A new method is presented for statistical modeling of three-dimensional torso shape for use in designing chairs and seats. Laser-scan data from a large-scale civilian anthropometric survey were extracted and analyzed using principal component analysis. Multivariate regression was applied to predict the average body shape as a function of overall anthropometric variables. For optimization applications, the statistical model can be exercised to randomly sample the space of torso shapes for automated virtual fitting trials. This approach also facilitates trade-off analyses and other the application of other design decision-making methods. Although seating is the specific example here, the method is generally applicable to other designing for human variability situations in which applicable body contour data are available.