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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.
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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