Determining Perceptual Characteristics of Automotive Interior Materials

• Published in: SAE International Journal of Materials and Manufacturing 2009, Vol.2 (1), p.1-11, Article 2009-01-0017
• Main Authors: Bhise, Vivek D, Mallick, Pankaj K, Sarma, Vishnuvardhan H
• Format: Article
• Language: English
• Subjects: Adjectives; Correlation coefficients; Evaluation; Linear regression; Material properties; Materials science; Materials selection; Musical intervals; Older people; Rating scales; Ratings; Ratings & rankings; Regression models; Research projects; Seats; Semantics; Sense of touch; Somatosensory perception; Visual perception
• ISSN: 1946-3979; 1946-3987; 1946-3987
• DOI: 10.4271/2009-01-0017

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. The subjects were also asked to provide: a) preference ratings on each of the material characteristic scales to indicate their “ideal preferred levels” (i.e. what level of each material characteristic they would ideally prefer for armrests and seats), and b) importance ratings to each of the material characteristics scales. In the third phase, the obtained perceptual ratings data were statistically analyzed to obtain: a) effects of subject age and gender on various perceptual ratings for different types of materials, and b) regression models to predict evaluative ratings (e.g. Dislike vs. Like) as functions of other perceptual material characteristics.