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Technical Paper

Effects of Material Type, Surface Roughness, Compressibility, Shape, Gender, Age and Sense Modality on Perception of Automotive Interior Materials

2010-04-12
2010-01-0682
This paper presents the results of an experimental study conducted to evaluate the effects of four material characteristics and two driver characteristics on the perception of automotive interior materials. The perceptual characteristics of the materials were measured using two sensing conditions, namely, visual sensing only and combined visual and tactile sensing. The experiments were conducted using the Taguchi's L16 orthogonal array with seven independent variables, namely material type, surface roughness, compressibility, driver's age, driver's gender, and sensing method. Twenty-four subjects participated in the experiments. Each subject was asked to evaluate four treatment combinations and provide ratings using seven 5-point semantic differential scales. In addition, physical measurements were made on surface roughness, coefficient of friction, and compressibility.
Technical Paper

Seat Comfort as a Function of Occupant Characteristics and Pressure Measurements at the Occupant-Seat Interface

2012-04-16
2012-01-0071
Seat comfort is a highly subjective attribute and depends on a wide range of factors, but the successful prediction of seat comfort from a group of relevant variables can hold the promise of eliminating the need for time-consuming subjective evaluations during the early stages of seat cushion selection and development. This research presents the subjective seat comfort data of a group of 30 participants using a controlled range of seat foam samples, and attempts to correlate this attribute with a) the anthropometric and demographic characteristics of the participants, b) the objective pressure distribution at the body-seat interface and c) properties of the various foam samples that were used for the test.
Technical Paper

Analysis and Optimization of Seat and Suspension Parameters for Occupant Ride Comfort in a Passenger Vehicle

2018-04-03
2018-01-1404
This study presents a methodology for comparative analysis of seat and suspension parameters on a system level to achieve minimum occupant head displacement and acceleration, thereby improving occupant ride comfort. A lumped-parameter full-vehicle ride model with seat structures, seat cushions and five occupants has been used. Two different vehicle masses are considered. A low amplitude pulse signal is provided as the road disturbance input. The peak vertical displacement and acceleration of the occupant’s head due to the road disturbance are determined and used as measures of ride comfort. Using a design of experiments approach, the most critical seat cushion, seat structure and suspension parameters and their interactions affecting the occupant head displacement and acceleration are determined. An optimum combination of parameters to achieve minimum peak vertical displacement and acceleration of the occupant’s head is identified using a response surface methodology.
Journal Article

Determining Perceptual Characteristics of Automotive Interior Materials

2009-04-20
2009-01-0017
This paper presents results of a three-phase research project aimed at understanding how future automotive interior materials should be selected or designed to satisfy the needs of the customers. The first project phase involved development of 22 five-point semantic differential scales to measure visual, visual-tactile, and evaluative characteristics of the materials. Some examples of the adjective pairs used to create the semantic differential scales to measure the perceptual characteristics of the material are: a) Visual: Light vs. Dark, Flat vs. Shiny, etc., b) Visual-Tactile: Smooth vs. Rough, Slippery vs. Sticky, Compressive vs. Non-Compressive, Textured vs. Non-Textured, etc., c) Evaluative (overall perception): Dislike vs. Like, Fake vs. Genuine, Cheap vs. Expensive, etc. In the second phase, 12 younger and 12 older drivers were asked to evaluate a number of different automotive interior materials by using the 22 semantic differential scales.
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