1. Gloss evaluation and prediction of achromatic low-gloss textured surfaces from the automotive industry
- Author
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S Baron, Caterina Passaro, Olivier Eterradossi, David Delafosse, J.S. Bidoret, School of Agriculture, Food Science and Veterinary Medicine, Pôle RIME - Recherche sur les Interactions des Matériaux avec leur Environnement (RIME), Centre des Matériaux des Mines d'Alès (C2MA), IMT - MINES ALES (IMT - MINES ALES), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-IMT - MINES ALES (IMT - MINES ALES), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT), Chambre d'agriculture du Loiret, Département Mécanique physique et interfaces (MPI-ENSMSE), École des Mines de Saint-Étienne (Mines Saint-Étienne MSE), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-SMS, Plasticité, Endommagement et Corrosion des Matériaux (PECM-ENSMSE), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-SMS-Centre National de la Recherche Scientifique (CNRS), and Centre Science des Matériaux et des Structures (SMS-ENSMSE)
- Subjects
business.industry ,General Chemical Engineering ,Automotive industry ,Human Factors and Ergonomics ,02 engineering and technology ,General Chemistry ,010402 general chemistry ,021001 nanoscience & nanotechnology ,01 natural sciences ,Gloss (optics) ,[SPI.MAT]Engineering Sciences [physics]/Materials ,0104 chemical sciences ,law.invention ,Optics ,Achromatic lens ,law ,Specular reflection ,0210 nano-technology ,business ,Reflectometry ,ComputingMilieux_MISCELLANEOUS ,Mathematics - Abstract
In this article, we investigate the benefit of including texture information in models of gloss perception of low-gloss textured achromatic plastic surfaces from the automotive industry. 4 models are compared: two gloss prediction models including texture information, one using data from reflectometry (M1) and one using data from goniophotometry (M2), and two models using data from reflectometry (M3) or goniophotometry (M4) alone. Both texture-corrected models (M1-M2) outclass the uncorrected intensity-based models, mainly because they are made texture invariant. Although the texture-corrected reflectometer-based prediction (M1) correlates rather well with sensory data, a more consistent fit is obtained by mixing textural to goniophotometric data (M2). This can be explained by the fact that contrast gloss is better than multiangle specular gloss at reflecting the observer's gloss evaluation strategy. © 2015 Wiley Periodicals, Inc. Col Res Appl, 2015
- Published
- 2015