Search Results

Seat lateral support is often talked about as a design parameter, but usually in terms of psychological perception. There are many difficulties in quantifying lateral support mechanically to the engineering teams: Anthropometric variation causes different people to interact with the seat in different places and at different angles, BPD studies are usually planar and don’t distinguish between horizontal support and vertical resistance to sinking in, most mechanical test systems are typically single-DOF and can’t apply vertical and horizontal loads concurrently, and there is scant literature describing the actual lateral loads of occupants. In this study, we characterize the actual lateral loading on example seating from various sized/shaped occupants according to dynamic pressure distribution. From this information, a six-DOF load and position control test robot (KUKA OccuBot) is used to replicate that pressure distribution.

Commercially available large-scale consumer surveys such as JD Power and Associates (JDPA) APPEAL Survey are often used to assess the success or failure of seat comfort design. Because access to the raw data from these surveys is limited, mean values are often the sole basis for analysis and comparison. Mean values alone, however, incompletely describe customer ratings that often lead to incorrect interpretation of the data. This can result in organizations expending capital to fix problems that do not exist. Since most companies have limited access to the raw data, this paper examines statistical and psychological factors that affect consumer survey results and describes methods for using these relationships to better interpret results of large scale, industry surveys.

In order to more accurately simulate the load distributions and histories experienced by automotive seats in field use, more biofidelic motion and loading devices are needed. Lear and KUKA have developed a system capable of controlling the coordinated motions of a pelvis, thighs and torso dummy in order to mimic human motions. The system takes kinematic data collected from human trials and converts them directly to a robot program. Additionally, simultaneous measures of human loading using pressure distribution mats can be obtained, and these measures are used as the basis for teaching the robot to correct the kinematic data using a neural net learning algorithm. The robot has direct and indirect load feedback integration that allows the load to be precisely maintained throughout the duration of a cycle test.

In order to more accurately simulate the load distributions and histories experienced by automotive seats in field use, more biofidelic motion and loading devices are needed. A new test dummy was developed by Lear Corporation and First Technology Safety Systems. This dummy uses exact skeletal geometry encased in a one-piece seamless mold with contours based on ASPECT data. A prototype was constructed and tested to demonstrate the efficacy of the concept. The skeleton and contour molds were created from CAD-generated rapid prototypes. The flesh was carefully formulated to have the mechanical properties of bulk muscular tissue in a state of moderate contraction, using data from the literature. This design allows much greater accuracy in reproducing human loads than was ever possible previously. The design has applications in durability, vibration and comfort testing.