Setting tolerances on color and texture for automotive coatings

Abstract

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

Model‐based corrections of geometric errors in multiangle measurements of gonio‐apparent coatings

“…However, they become more moderate along with the change of d from 20. 5 to 0.5 particularly in Xirallic Blue, which is consistent with Fig. 7 showing the |d p | larger than the |d n | for this specimen.…”

“…As Table III shows, the eight variations g s,v (v 5 1-8) are displaced from the original g s,0 5 g s by 10. 5 or 20.5 in the illuminating and/or detecting directions in the air. Those 117 (9 3 13) geometries g s,v were realized by GCMS-4 Gonio-spectrophotometer of Murakami Color Research Inc. (Tokyo, Japan) which was confirmed to have repeatability below 0.001 in angle setting in advance.…”

“…[1][2][3][4] However, such measurements are very sensitive to angular errors introduced in the geometry to the extent that the measurements are often unreproducible and unreliable particularly in the near-specular (highlight) subgeometries. 5 There are two major angular errors: one is an irregular geometry (nonconformance with the reference geometry) introduced mostly in the production of instruments, and the other is a positioning error, a tilt of the geometry in the incident plane (plane including incident and reflection optical axes), against the specimen introduced at the time of each measurement. The effects of the former are different for each direction in the multiangle geometry but remain unchanged after the initial occurrence, while the effects of the latter are uniform for all directions but varies with each measurement.…”

Measuring color differences in automotive samples with lightness flop: A test of the AUDI2000 color-difference formula

“…Optimized coefficients in Table 2 reduced STRESS value from 25.1 to 21.8 and WSTRESS value from 26.6 to 24.0. As we can see, Table 2 shows enough similar values for coefficients a 1 and a 2 but different values for coefficient a 3 , although in fact this is not very relevant because samples in our experiment have very low C* ab,45 values and the term including the coefficient a 3 is almost negligible. The values for coefficients a 4 and a 6 in Table 2 are also enough divergent in the two optimizations.…”

“…In industrial applications related to quality control it is important to use appropriate color-difference formulas to accurately predict subjective assessments of color differences performed by colorists or final users. For example, in the automotive industry, the different parts of cars are produced by different manufactures using different materials, and reliable color-difference formulas are necessary for automatic pass/fail color decisions [2,3]. During the last 40 years advances have been made in the development of successful colordifference formulas for industrial applications [4], and the last CIE-recommended colordifference formula CIEDE2000 [5] has been recently adopted as a joint CIE/ISO standard [6].…”