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Multisensory Contributions to Perceived Quality and Authenticity of Materials for the Vehicle Interior
ISSN: 0148-7191, e-ISSN: 2688-3627
Published March 28, 2017 by SAE International in United States
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Material authenticity is an important factor for appearance and perceived quality of the vehicle interior. The term authenticity implies ambivalence: For the product designer, it means identification and trueness of the origin of the material. The customers, however, can only access information on the nature of the materials via their own perception of surface features. Thus, the intended authenticity of a material always needs to be conveyed by its surface. Specific cases illustrate the context: 1. The customer touches a part of known matter, but various layers prevent from directly touching the natural material: e.g. leather at the steering wheel, applications of wood. 2. Perception of a thin surface layer indicates authentic material, which is not fulfilled by the whole part: e.g. plastic parts plated with metal. 3. A part consists of authentic material, but newly composed, so that it is not easily identified, such as recycled materials, e.g. leather fiber layers for seats. Optimization of the perceivable authenticity is always a multi-sensory task. Customers see and touch materials in the show room. Sound is usually generated by touching surfaces. Smell is important for natural materials, like leather. Even if the material is only observed visually, its appearance points towards tactile features, like softness, roughness, etc. Beside general considerations, multi-sensory perception of leather and vinyl materials as well as haptic appearance of thin metal layers is described in detail. An innovative method for measurement of the contact temperature is introduced, which helps to evaluate the materials potential to provide authentic “metal feel”.
CitationHaverkamp, M. and Moos, A., "Multisensory Contributions to Perceived Quality and Authenticity of Materials for the Vehicle Interior," SAE Technical Paper 2017-01-0494, 2017, https://doi.org/10.4271/2017-01-0494.
Data Sets - Support Documents
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