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A robust design procedure is applied to achieve improved vehicle handling performance as an integral part of simulation-based vehicle design. This paper presents a hybrid robust design method, the robust design process strategy (RDPS), which makes full use of the intense complementary action of characteristics between the Response Surface Methodology (RSM) and the Taguchi method, to get the robust design of the vehicle handling performance. The vehicle multi-body dynamic model is built in the platform that is constructed by the software of iSIGHT, ADAMS/CAR, and MATLAB. The design-of-experiment method of the Latin Hypercube (LHC) is used to obtain the approximate area values, and then the RDPS is utilized to achieve improved vehicle handling performance results. The validation is made by the Monte Carlo Simulation Technique (MCST) in terms of the effectiveness of the RDPS in solving robust design problems.

Human models are viable methods of introducing human factors and ergonomic objectives into the design process at an early stage. Used correctly, they allow users to simulate and analyze potential human-machine interactions saving time and money. As with any model, mistakes can be made. The primary sources of error stem from incorrect use and misinterpretation of the results by the analyst. The development of three-dimensional human modeling software has only compounded these issues by adding a digital subject, itself a human model. This complicates the interpretation and use of these tools by layering one human model on top of another. The purpose of this paper is to highlight common categories of misuse and misinterpretation of digital human models as well as to propose a method for improving user understanding of human models through formal documentation of critical components.

This paper presents an optimization-based algorithm for simulating the dynamic motion of a digital human. We also formulate the metabolic energy expenditure during the motion, which is calculated within our algorithm. This algorithm is implemented and applied to Santos™, an avatar developed at The University of Iowa. Santos™ is a part of a virtual environment for conducting digital human analysis consisting of posture prediction, motion prediction, and physiology studies. This paper demonstrates our dynamic motion algorithm within the Santos™ virtual environment. Mathematical evaluations of human performance are essential to any effort to compare various ergonomic designs. In fact, the human factors design process can be formulated as an optimization problem that maximizes human performance. In particular, an optimal design must be found while taking into consideration the effects of different motions and hand loads corresponding to a number of tasks.

Santos™, a digital human avatar developed at The University of Iowa, exhibits extensive modeling and simulation capabilities. Santos™ is a part of a virtual environment for conducting human factors analysis consisting of posture prediction, motion prediction, and ergonomics studies. This paper presents part of the functionality in the Santos™ virtual environment, which is an optimization-based algorithm for simulating dynamic motion of Santos™. The joint torque and muscle power during the motion are also calculated within the algorithm. Mathematical cost functions that evaluate human performance are essential to any effort that would evaluate and compare various ergonomic designs. It is widely accepted that the ergonomic design process is actually an optimization problem with many design variables. This effort is basically a task-based approach that believes humans assume different postures and exert different forces to accomplish different tasks.