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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.

Due to the challenges in quantifying hand loads in manufacturing environments it is often assumed that the load is evenly distributed between the hands, even when handling parts with non-uniform mass distribution. This study estimated hand loads for six female subjects, when handling a custom part in 8 different configurations (2 weights, 4 CofM locations). The calculated hand loads varied from 20 to 50% of the weight being handled. The magnitude of asymmetrical hand loading depended on both the part orientation and the location of the CoM. Based on this study the knowledge of part weight, CofM location and hand positioning will allow the users of digital human models to perform more realistic and reliable task analysis assessments as the force distributions will be more representative of the actual loading rather than simply assuming the load is evenly distributed between the hands.

The use of digital human models (DHM) to perform geometric evaluations of hand clearances and reach zones has become common practice at Ford Motor Company. Moreover, DHMs have also been used for performing strength evaluations to ensure ergonomically acceptable jobs. A process called Hose Connections Acceptability Ratings (HCAR) was developed to establish insertion force targets in the early phases of product design. Once targets are set, design and release engineers provide design intent data to achieve sign off from manufacturing engineering. The process is complete when the hose efforts are confirmed at physical part validation build events.

Digital Human Models are used extensively in virtual manufacturing to evaluate hand clearance and reach. Spatial assessments of accommodation are typically conducted using digital human models representative of the manufacturing population. Unfortunately, these models are often based on anthropometry gathered from sources that are not representative of the actual target worker population. For example, the size and shape might be based on data from the U.S. military, which differs in .fitness, age, and race distributions from the typical automotive manufacturing population. Ford ergonomists traced errors in accommodation predictions to these inaccurate representations. Using a recently developed statistical methodology incorporating principal components analysis, the anthropometry of the target worker population was synthesized. Using these new data, Ford updated the anthropometry of their digital human models to reflect changes due to secular trends in the U.S.

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.

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”.