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It is generally accepted that all postures obtained from motion capture technology are realistic and accurate. Physical props are used to enable a subject to interact more realistically within a given virtual environment, yet, there is little data or guidance in the literature characterizing the use of such physical props in motion capture studies and how these effect the accuracy of postures captured. This study was designed to evaluate the effects of various levels of physical prop complexity on the motion-capture of a wide variety of automotive assembly tasks. Twenty-three subjects participated in the study, completing twelve common assembly tasks which were mocked up in a lab environment. There were 3 separate conditions of physical props: Crude, Buck, and Real. The Crude condition provided very basic props, or no props at all, while the Buck condition was a more elaborate attempt to provide detailed props. Lastly, the Real condition included real vehicle sections and real parts.

Automotive manufacturing presents unique challenges for ergonomic analysis. The variety of tasks and frequencies are typically not seen in other industries. Moving these challenges into the realm of digital human modeling poses new challenges and offers the opportunity to create and enhance tools brought over from the traditional reactive approach. Chiang et al. (2006) documented an enhancement to the Siemen's Jack Static Strength Prediction tool. This paper will document further enhancements to the ErgoSolver (formerly known as the Ford Static Strength Prediction Solver).

Assembly operators are rarely observed performing one-handed tasks where the unutilized hand is entirely inactive. Therefore, this study was designed to determine the forces applied to supporting hands, by automotive assembly operators, during common one-handed tasks such as hose installations or electrical connections. The data were computed as a percentage of body weight and a repeated measures analysis of variance (ANOVA) (p<0.05) was conducted. Supporting hand forces were observed to range from 5.5% to 12.1% of body mass across a variety of tasks. The results of this study can be used to account for these supporting hand forces when performing a biomechanical/ergonomic analysis.

Often, ergonomists need to determine the maximum acceptable load or force for a given task. Ergonomic tools, like the NIOSH Lifting Guidelines (Waters et al, 1993) and the Liberty Mutual Tables (Snook & Ciriello, 1991)), provide such loads for selected population percentiles. In contrast, the UGS Jack Static Strength Prediction tool (JSSP), based on the University of Michigan’s 3D Static Strength Prediction Program (3DSSPP), uses force(s) as inputs and calculates the percentage of the male or female population that would be capable (%Cap) for a given task. Typically, the %Cap threshold will be a fixed number determined from corporate or government guidelines (e.g. 75% of females). Thus, in order to find the acceptable load, users of JSSP must iterate through loads until they find a %Cap that is just below their predetermined threshold.

This study addressed the effect of scaling subjects in a virtual reality (VR) environment when performing ergonomic evaluations for assembly automotive tasks. Subjects were selected to fit into one of 4 anthropometric groups, ranging in size from a 5th percentile female to a 95th percentile male. Each subject performed 3 tasks while interacting with a digital rendering of a vehicle. Subjects were represented as a human manikin (Classic Jack 4.0, UGS) whose actions were driven by motion tracking (EvaRT, MotionAnalysis). Each subject performed the tasks under 4 different conditions; once with unscaled anthropometry and in three conditions where they were made to appear either larger or smaller than their actual height. Peak and cumulative low back loads and shoulder moments, as well as joint angles, were calculated and compared (ANOVA). In some cases subject scaled to a particular size performed differently than those that were actually that size.

Henry Ford is credited with the invention of the assembly line and for 100 years now we have manufactured high quality cars and trucks. The process to bring cars and trucks into production has seen many changes with the introduction of new technology, however the principle is still the same; designers draw concept designs and engineers transform these designs into functional parts. The first time the engineering community has a real feel for the design and process compatibility is at a physical prototype build. The money invested in the designs and prototype parts alone make the thought of a design change this late in the game, unbearable. The design of the manufacturing process along with product design has embraced virtual tools and digital human models to assess assembly feasibility. The major incentive to utilizing such tools is to reduce costly re-engineering of parts and to decrease prototype costs. Virtual technology allows ergonomists and engineers to perform “virtual builds”.