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Industrial tasks performed by standing workers are among those most commonly simulated using digital human models. Workers often walk, turn, and take acyclic steps as they perform these tasks. Current h uman modeling tools lack the capability to simulate these whole body motions accurately. Most models simulate walking by replaying joint angle trajectories corresponding to a general gait pattern. Turning is simulated poorly if at all, and violations of kinematic constraints between the feet and ground are common. Moreover, current models do not accurately predict foot placement with respect to loads and other hand targets, diminishing the utility of the associated ergonomic analyses. A new approach to simulating stepping and walking in task-oriented activities is proposed. Foot placements and motions are predicted from operator and task characteristics using empirical models derived from laboratory data and validated using field data from an auto assembly plant.

The pulleys employed in automotive accessory drive systems very often consist of a two piece assembly; a multitude of fastening techniques are used in completing the assembly. There are numerous assembly methods and a variety of distinct pulley configurations dictated by the various automobile manufacturers in accordance with individual accessory drive needs. An expert system is being developed to evaluate the merit of multiple assembly alternatives for a specific pulley application. The expert system provides a consistent evaluation tool for assembly alternatives, balancing the influence of product cost, strength and quality considerations. The knowledge-based system is implemented in an expert system shell called AGNESS (A Generalized Network-based Expert System Shell). The expert system judges the acceptability of various pulley assembly techniques, assigning a high “merit value” to the better designs and proportionately lower values to less desirable designs.

The need for improved understanding of new magnesium alloys for the automotive industry continues to grow as the application for these lightweight alloys expands to more demanding environments, particularly in drivetrain components. Their use at elevated temperatures, such as in transmission cases, presents a challenge because magnesium alloys generally have lower creep resistance than aluminum alloys currently employed for such applications. In this study, a new die cast magnesium alloy, MEZ, containing rare earth (RE) elements and zinc as principal alloying constituents, was examined for its bolt-load retention (BLR) properties. Preloads varied from 14 to 28 kN and test temperatures ranged from 125 to 175°C. At all test temperatures and preloads, MEZ retained the greatest fraction of the initial imposed preload when compared to the magnesium alloys AZ91D, AE42, AM50, and the AM50+Ca series alloys.

This paper presents an extension of our previous work on decomposition-based assembly synthesis [1], where the 3D finite element model of a vehicle body-in-white (BIW) is optimally decomposed into a set of components considering the stiffness of the assembled structure under given loading conditions, and the manufacturability and assemblability of each component. The stiffness of the assembled structure is evaluated by finite element analyses, where spot-welded joints are modeled as linear torsional springs. In order to allow close examinations of the trade-off among stiffness, manufacturability, and assemblability, the optimal decomposition problem is solved by multi-objective genetic algorithm [2,3], with graph-based crossover [4,5], combined with FEM analyses, generating Pareto optimal solutions. Two software programs are developed to implement the proposed method.

Warpage is the distortion induced by inhomogeneous shrinkage during injection molding of plastic parts. Uncontrolled warpage will result in dimensional instability and bring a lot of challenges to the mold design and part assembly. Current commercial simulation software for injection molding cannot provide consistently accurate warpage prediction, especially for semi-crystalline thermoplastics. In this study, the root cause of inconsistency in warpage prediction has been investigated by using injection molded polypropylene plaques with a wide range of process conditions. The warpage of injection molded plaques are measured and compared to the numerical predictions from Moldex3D. The study shows that with considering cooling rate effect on crystallization kinetics and using of the improved material model for residual stress calculations, good agreements are obtained between experiment and simulation results.