Comparison of the metrics between the CIELAB and the DIN99 uniform color spaces using dental resin composite material values

Lee, Yong Keun; Powers, John M.

doi:10.1002/col.20206

Cited by 4 publications

(11 citation statements)

References 16 publications

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“…There was significant correlation in each shade guide, in which the coefficients of determination (r 2 ) were 0.94 and 0.93, respectively. Δ E* ab and Δ E 00 were correlated regardless of the signs of the CIE a * value of the compared pairs, as in previous studies 14–17 . In other words, Δ E 00 values between shade tabs of different signs of the CIE a * value were correlated to Δ E* ab and to Δ E 99 values.…”

Section: Discussionsupporting

confidence: 80%

“…According to previous studies in which the color differences after polymerization or thermocycling of the same shade of dental resin composites were compared, three color‐difference values (Δ E* ab , Δ E 99 , Δ E 00 ) were correlated to one another 14–17 . Paravina et al reported that there was a very strong correlation (r > 0.97) between the Δ E* ab and Δ E 00 values, indicating that the limitations of the CIELAB system do not appear to be a problem in the evaluation of polymerization‐dependent color changes of dental resin composites 14 .…”

mentioning

confidence: 99%

“…It was suggested that two color‐difference values (Δ E* ab , Δ E 00 ) can be used interchangeably for the evaluation of color difference after polymerization and thermocycling of resin composites 16 . After polymerization and thermocycling of resin composites, significant correlations between Δ E* ab values and Δ E 99 values were reported (r = 0.68 to 0.77) 17 . In the evaluation of color‐difference formulas in previous studies 14–17 in the dental field, the shades investigated were limited to those of resin composites with the same shade designation (same hue), or the same CIE a * signed pairs.…”

mentioning

confidence: 99%

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Correlations between Color Differences Based on Three Color‐Difference Formulas Using Dental Shade Guide Tabs

Kim

Lee

2009

Journal of Prosthodontics

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Section: Discussionsupporting

confidence: 80%

mentioning

confidence: 99%

mentioning

confidence: 99%

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Correlations between Color Differences Based on Three Color‐Difference Formulas Using Dental Shade Guide Tabs

Kim

Lee

2009

Journal of Prosthodontics

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“…Cui and Luo presented several modifications to the DIN99 space, improving the description of color differences. Independent tests of modification DIN99d show a performance comparable to the best of the other color difference equations . A more recent modification is the “optimized DIN99” color difference equation DIN99o.…”

Section: Some Current Issues In Setting Qc Color Tolerancesmentioning

confidence: 87%

Setting tolerances on color and texture for automotive coatings

Kirchner

Ravi²

2012

Color Research & Application

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In the automotive industry, color quality control is increasingly done by reflection measurements. We discuss how color tolerances are set in specifications to suppliers of add‐on parts and to paint suppliers. We mention several factors that often lead to unrealistically tight settings, and therefore to incorrect rejections and unnecessary high productions costs. We show that this is likely to occur when the dEab color difference equation is used, or when a strict criterion separating pass from fail is used instead of specifying a “grey area” where instrumental monitoring needs to be followed by visual assessments. Unrealistically, tight tolerances also result from halving tolerances in the supply‐customer chain in an attempt to compensate color variations due to uncontrolled application conditions. Tolerances should be widened further when a gap separates an add‐on part from the car body, making visual discrimination of color differences less critical. Other common situations where tolerances should be widened are the presence of visual texture in effect coatings, the lightness of metallic coatings becoming very high (L*> 100) and measurement geometries close to the gloss angle. Finally, we address the issue that instrumental color tolerances should not be tighter than what is allowed by instrumental reproducibility, repeatability, and inter‐instrument agreement. Accounting for these factors, we provide a set of reasonable values for tolerances on color and on visual texture parameters, based on our own practical experience. But realistic tolerance values depend very much on actual conditions, and should be agreed in tripartite discussions among automotive industry, suppliers of add‐on parts, and paint supplier. © 2012 Wiley Periodicals, Inc. Col Res Appl, 39, 88–98, 2014

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“…Accordingly, the use of a recipe for mixing the raw material does not guarantee that the colour of the blend is strictly "close" to the desired colour. This means that the colorimetric distance between a reference and the blend, measured by means of the CMC distance [1,2] under different standard illuminants [3], may result too greatly. As widely known, the CMC tolerancing system has been developed by the Colour Measurement Committee of the Society of Dyers and Colourists in Great Britain and may be defined as a modification of CIELAB [4] which provides better agreement between visual assessment and instrumentally measured colour difference.…”

Section: Introductionmentioning

confidence: 99%

Colour Mixing Modelling and Simulation: Optimization of Colour Recipe for Carded Fibres

Furferi

Carfagni

2010

Modelling and Simulation in Engineering

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Colour matching between carded and finished fibres is an important challenge for textile industry. The straightforward approach for mixing together some differently coloured fibres in order to obtain a blend of a desired colour is to perform a trial and error approach starting from a given colour recipe and optimizing it with several attempts. Unfortunately, dyeing process so as the carding procedure may result in a carded fibre whose colour is different from the desired one. As a consequence textile companies have to modify the original recipe in order to reduce the gap between the colour of the final product and the desired one. The present work describes a model able to simulate the colour mixing of fibres in order to assess the best recipe. The model consists in two modules: a "prediction module" predicts the colour of a blend obtained by mixing together several fibres; an "optimization module" is used to optimize the final recipe. The devised system has been tested for optimizing the recipe of a set of 200 blends. The mean error in predicting the blend colour is about 15% with a variance of 0.165. The time for optimizing the recipe is reduced by 92%.

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Comparison of the metrics between the CIELAB and the DIN99 uniform color spaces using dental resin composite material values

Cited by 4 publications

References 16 publications

Correlations between Color Differences Based on Three Color‐Difference Formulas Using Dental Shade Guide Tabs

Correlations between Color Differences Based on Three Color‐Difference Formulas Using Dental Shade Guide Tabs

Setting tolerances on color and texture for automotive coatings

Colour Mixing Modelling and Simulation: Optimization of Colour Recipe for Carded Fibres

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