Matthew B. Parkinson

Multicriteria Optimization in Product Platform Design

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.

Optimizing Vehicle Occupant Packaging

Occupant packaging practice relies on statistical models codified in SAE practices, such as the SAE J941 eyellipse, and virtual human figure models representing individual occupants. The current packaging approach provides good solutions when the problem is relatively unconstrained, but achieving good results when many constraints are active, such as restricted headroom and sightlines, requires a more rigorous approach. Modeling driver needs using continuous models that retain the residual variance associated with performance and preference allows use of optimization methodologies developed for robust design. Together, these models and methods facilitate the consideration of multiple factors simultaneously and tradeoff studies can be performed. A case study involving the layout of the interior of a passenger car is presented, focusing on simultaneous placement of the seat and steering wheel adjustment ranges. Tradeoffs between adjustability, driver accommodation, and exterior vision are explored under this paradigm. These results are contrasted with those obtained using boundary manikins.

Optimizing Truck Cab Layout for Driver Accommodation

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.

A New Approach to Modeling Driver Reach

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 drivers 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 ia a maximally difficult reach. The new approach is made possible by new measurement methods that allow detailed and efficient sampling of an individuals reach-difficulty function. This paper summarizes the experimental approach and presents the structure of the new integrated model of population reach difficulty and capability.

ADAPTIVE EXPERIMENTAL DESIGN APPLIED TO AN ERGONOMICS TESTING PROCEDURE

University of MichiganAnn Arbor, Michigan 48109{msasena, mparkins, goovaert, pyp, mreed}@umich.eduABSTRACTNonlinear constrained optimization algorithms are widelyutilized in artifact design. Certain algorithms also lend them-selves well to design of experiments (DOE). Adaptive designrefers to experimental design where determining where to sam-ple next is influenced by information from previous experiments.We present a constrained optimization algorithm known as su-perEGO (a variant of the EGO algorithm of Schonlau, Welchand Jones) that is able to create adaptive designs effectively. Itsability to allow easily for a variety of sampling criteria and to in-corporate constraint information accurately makes it well suitedto the needs of adaptive design. The approach is demonstratedon a human reach experiment where the selection of samplingpoints adapts successfully to the stature and perception of the in-dividual test subject. Results from the initial study indicate thatsuperEGO is able to create experimental designs that yield moreaccurate models using fewer points than the original testing pro-cedure.NOMENCLATUREDOE Design of ExperimentsISC Infill Sampling CriterionMLE Maximum Likelihood Estimation

MODELING VARIABILITY IN TORSO SHAPE FOR CHAIR AND SEAT DESIGN

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.

The role of anthropometry in designing for sustainability

An understanding of human factors and ergonomics facilitates the design of artefacts, tasks and environments that fulfil their users’ physical and cognitive requirements. Research in these fields furthers the goal of efficiently accommodating the desired percentage of user populations through enhanced awareness and modelling of human variability. Design for sustainability (DfS) allows for these concepts to be leveraged in the broader context of designing to minimise negative impacts on the environment. This paper focuses on anthropometry and proposes three ways in which its consideration is relevant to DfS: reducing raw material consumption, increasing usage lifetimes and ethical human resource considerations. This is demonstrated through the application of anthropometry synthesis, virtual fitting, and sizing and adjustability allocation methods in the design of an industrial workstation seat for use in five distinct global populations. This work highlights the importance of and opportunities for using ergonomic design principles in DfS efforts. Practitioner Summary: 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.

A comparison of methodologies for designing for human variability

In the design of artefacts that interact with people, the spatial dimensions of the user population are often used to size and engineer the artefact. The variability in anthropometry indicates the fixed allocation of space, adjustability requirements, or how many sizes are needed to accommodate the intended user population. Various tools are used to achieve this goal, including boundary manikins, digital human models, prototypes and population models, and hybrid methods that combine the approaches. The present work explores each of these and their relative strengths and weaknesses. This is done in the context of a univariate case study involving the adjustability requirements of a stationary bicycle. An experiment involving 51 individuals was conducted to obtain the data necessary for utilising and evaluating the methods.

Improving an Ergonomics Testing Procedure via Approximation-based Adaptive Experimental Design

Adaptive design refers to experimental design where the next sample point is determined by information from previous experiments. This article presents a constrained optimization algorithm known as superEGO (a variant of the EGO algorithm of Schonlau, Welch, and Jones) that can create adaptive designs using kriging approximations. Our primary goal is to illustrate that superEGO is well-suited to generating adaptive designs which have many advantages over competing methods. The approach is demonstrated on a novel human-reach experiment where the selection of sampling points adapts to the individual test subject. Results indicate that superEGO is effective at satisfying the experimental objectives.

PREDICTING 5TH AND 95TH PERCENTILE ANTHROPOMETRIC SEGMENT LENGTHS FROM POPULATION STATURE

Designing for human variability frequently necessitates an estimation of the spatial requirements of the intended user population. These measures are often obtained from “proportionality constants” which predict the lengths of relevant anthropometry using stature. This approach is attractive because it is readily adapted to new populations—only knowledge of a single input, stature, is necessary to obtain the estimates. The most commonly used ratios are those presented in Drillis and Contini’s report from 1966 [1]. Despite the prevalence of their use, these particular values are limited because the size and diversity of the population from which these ratios were derived is not in the literature, and the actual body dimensions that each ratio represents are not clear. Furthermore, they are often misinterpreted and used inappropriately. This paper introduces a new approach, the “boundary ratio” which mitigates many of these issues. Boundary ratios improve on the traditional application of proportionality constants by: 1) explicitly defining the body dimensions, 2) defining constants for the 5th , 50th , and 95th percentile measures, and 3) providing distinct constants for males and females when necessary. This approach is shown to better model the range of variability exhibited in population body dimensions.Copyright