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Derivation of an instrumentally based geometric appearance index for the automotive industry.

Abstract In the present study, attempts were made to develop an index of geometric appearance capable of accurately predicting and quantifying the visually perceived geometric aspects of appearance of achromatic automotive finishes. To this end, three previously prepared individual scales for the three most significant geometric appearance attributes, namely specular gloss, distinctness of image (DOI), and orange peel for each of a series of metallic black, metallic gray, metallic silver, and solid white automotive finishes were utilized. The differences in each attribute were quantified visually by a panel of 16 observers, in terms of a lightness difference of an also previously prepared lightness scale. The innovative use of a common lightness scale showed that there is a surprisingly good correlation between the instrumentally measured specular gloss, DOI, and the LW parameter of the Wave scan instrument and the corresponding visually evaluated equivalents at the four investigated achromatic levels through minimizing observer errors and enhancing accuracy of the perceptibility procedure. These high accuracies made provisions for the implementation of the principle of additivity which led to the derivation of a geometric appearance index (GAI). However, before its derivation, one instrumentally measured parameter was remodeled exponentially to define an innovative parameter chosen to be named "percent absence of orange peel." The proposed GAI illustrates high correlation with visual assessments and is herewith recommended as a stand-alone index for predicting the geometric appearance of automotive finishes. Furthermore, this index together with a chromatic appearance index could form the foundation for deriving a total appearance index for the automotive industry.

Keywords Geometric appearance index, Gloss, Distinctness of image, Absence of orange peel, Visual assessment, Automotive finish

Introduction

Appearance control is a challenging concept for automotive manufacturers and the suppliers of appearance measuring instruments as well as for the pure and applied researchers. Just like any other industry in which appearance plays a decisive role, appearance control in the automotive industry is indispensable for meeting customers' modern demands. The exterior appearance of a car, which is mainly influenced by the quality of the surface coating used as well as the process by which such a coating is applied, greatly affects customers' appreciation of the quality of such a vehicle as a whole. (1-3) As a result, evaluation of appearance of an automotive finish is of utmost importance. The necessity for developing the science of "appearance measurement" in the automotive industry has been realized by automotive manufacturers for many years.

Color as well as geometric characteristics such as gloss, distinctness of image (DOI), orange peel (OP), etc. are the most significant appearance attributes in the automotive industry and the correlation between visual and instrumental assessments has been the topic of several research investigations. (4-14)

Color as defined by the CIE system of color measurement is an appearance attribute capable of being measured by the use of conventional or specially designed spectro- and/or goniospectro-photometers. (15-17) Geometric appearance attributes, on the other hand, which are associated with the way in which white light is reflected from a surface at various angles of observation, are only vaguely defined and much more complicated to assess visually or instrumentally.

Although limited work has been conducted on modeling visual perception of some individual geometric attributes such as gloss, DOI, and OP, (18-24) only a few studies have concentrated on quantifying the total geometric appearance attribute. Even far fewer investigations have been carried out on the total appearance of automotive coatings as compared to visual assessments.

Dekker et al. (25) have investigated the perceptual combination of visual texture parameters namely diffuse coarseness and glint impression and color of special effect automotive coatings. An acceptability-based visual assessment technique was used to score visual differences in color, glint impression, and diffuse coarseness of some prepared sample pairs. A semi-continuous total visual appearance score (CDG) defined by the summation of visual scores for each of the three properties (i.e., color, diffuse coarseness and glint impression) was calculated for each sample pair. They used a linear combination of instrumentally measured color and texture differences of each sample pair (using the BYK-mac instrument) to define a total appearance difference formula (TADiF). The correlation between CDG as the dependent variable and the corresponding TADiF values were analyzed in an attempt to determine the actual TADiF for the automotive coatings under test.

In a similar work carried out by Haung et al., (26) the total appearance differences of some achromatic and chromatic metallic paint samples were predicted as a function of instrumentally measured color and texture properties. The gray scale usually utilized for color fastness determinations was employed to visually evaluate the total difference as well as individual differences in color, coarseness, and glint of the sample pairs. Finally, they derived a formula in an attempt to predict the total visual difference ([DELTA]T) of each sample pair in terms of the measured [DELTA][L.sup.*], [DELTA][a.sup.*], and [DELTA][b.sup.*] (based on the CIE 1976 color difference equation), [DELTA]Glint (for directional illumination), and [DELTA]Coarseness (for diffused illumination). However, Dauser (27) has since shown that glint has much lower correlation with visual assessments compared to coarseness.

Additionally, in a recent study, the present authors (20) have shown that the BYK's Balance Index does not correlate well with visual assessments of achromatic automotive coatings. Such studies illustrate that the automotive industry is still deprived of a suitable geometric appearance index (GAI) as well as a beneficial total appearance index.

In the present investigation, the derivation of a GAI based on instrumentally measured parameters with high correlation scores with visual assessments is sought. In our previous study, (28) high correlations were attained between visually assessed specular gloss, DOI, and OP, and their instrumentally measured equivalents. The same 12 scales (i.e., prepared physical scales for each of the three geometric attributes mentioned above at four levels of achromaticity) in addition to the prepared common color constant lightness scale were also employed in the present study in order to exploit the "principle of additivity" and provisions for the derivation of a GAI. However, it must be noted that since the measured parameter LW of the wave scan instrument is inversely proportional to the other two appearance attributes (i.e., specular gloss and DOI), a new parameter we chose to call "absence of orange peel" (AOP) was first derived before the execution of the principle of additivity in order to derive a general index for geometric appearance. Finally, preliminary tests were carried out in order to equate differences in each of the three individual measured geometric attributes to lightness differences in the common lightness scale for tolerancing purposes.

Experimental

Preparation of appearance attribute scales

Twelve sets of physical scales for specular gloss (G), distinctness of image (DOI), and orange peel (OP) were prepared according to the procedure fully described in our previous work. (28) Four achromatic automotive basecoats, namely metallic black, metallic gray, and metallic silver, as well as a solid white paint expected to have different optical properties were selected to prepare three individual attribute scales (i.e., specular gloss, DOI, and OP) at these four levels of achromaticity. All samples were prepared using phosphated, electrocoated, and primered 20 x 10 [cm.sup.2] steel substrates at the Iran Khodro car manufacturing company (Iran). The subsequent clearcoat layer to be applied on each basecoat was manipulated by incorporating various amounts of fine grained after-treated precipitated silica as a matting agent, and by adjusting the application parameters such as viscosity, thickness of the clearcoat, and the spraying distance to obtain various levels of specular gloss, DOI, and OP in the respective scales.

For preparing the Specular Gloss scale, the amount of a precipitated silica as a matting agent was manipulated in a single-layer solventborne acrylic/polyurethane clearcoat. In this way, 12 samples were prepared, separately for each achromatic level, all having specular gloss values between around 65 and 90 and DOI values higher than 90, from which five samples were selected to constitute the Specular Gloss scales. Table 1 presents the instrumentally measured specular gloss, DOI, and LW parameters of the Specular Gloss scale samples. The samples in Table 1 are coded based on their sets and achromatic level. For instance, GS3 refers to the third silver paint panel in the Specular Gloss scale.

As can be seen in Table 1, each scale is prepared in such a way that the samples vary significantly in specular gloss, while the other two attributes (i.e., DOI and LW) are kept constant as much as possible.

Preparing the DOI scale was the most challenging step. For this scale, two separate clearcoat layers were consecutively applied on the basecoat layer by the wet on wet process. The first clearcoat layer contained the matting agent to adjust the desired DOI value, while the second clearcoat layer, applied directly on top of the first clearcoat layer, was bare (without any matting agent) to compensate the gloss loss of the sample due to the presence of the matting agent in the first clearcoat layer. In this way, 15 samples were prepared, separately for each achromatic level, all having DOI values between around 60 and 98 and specular gloss values between around 80 and 90, from which 5-6 samples were selected to constitute the DOI scales. The instrumentally measured specular gloss, DOI, and LW parameters of the DOI scale samples are depicted in Table 2.

Finally, the OP scales were prepared by manipulating the application parameters such as viscosity, thickness of the single-layer clearcoat, and mainly the spraying distance. Since there was no matting agent in the clearcoat layer, all the samples had specular gloss and DOI values higher than 85, while the LW parameter varied between around 2 and 50. Table 3 presents the instrumentally measured specular gloss, DOI, and LW values of the OP scale samples.

In this way, 74 achromatic paint panels were prepared at four achromatic levels which constituted 12 individual scales, each varying in only one attribute, with the other two attributes being kept constant as much as possible. Such one-dimensionality of each scale, schematically described in our previous work, (28) would guarantee that the visual perception of each attribute would be least influenced by the other attributes. The LW parameter of the BYK-Gardner Wave scan which attained the highest correlation with visual evaluations of OP in our previous study (28) was selected in the present work as the most likely instrumental representation of the visually assessed OP.

Instrumental characterization

The three most conventional, reasonably priced, and commercially available appearance measuring instruments in the automotive industry were employed in this work for measuring specular gloss, DOI, and OP of the prepared paint panels.

The BYK-Gardner micro-Tri-gloss glossmeter (BYK-Gardner, Germany) was used to measure specular gloss values of paint panels according to the ASTM standard D523. (29) The Novo-Gloss I.Q. Gonio-photometer (Rhopoint Instruments, United Kingdom) was employed to determine the DOI values of the paint panels according to the ASTM standard E430. (30) The physical waviness of the paint panels better known as "orange peel" was determined in terms of the LW parameter utilizing the BYK-Gardner Wave scan DOI (BYK-Gardner, Germany).

The designed common lightness scale for visual assessments

An innovative visual assessment technique based on quantification of differences in geometric appearance attributes in terms of a designed color constant lightness scale, proposed, and utilized in our previous investigation, (28) was also employed in this work to perform the visual assessment experiments. The designed lightness scale was composed of eight 10 x 20 [cm.sup.2] gray matte polyester fabrics having almost the same chromaticities and varying only in lightness values. (28) The reason for designing such a color constant lightness scale for quantifying the differences in any geometric attribute is based on the well-known fact that perception by the aid of the eye/brain combination is basically a zero balancing operation. (31,32) In other words, the human perception system is a qualitative detector of differences and, as such, is incapable of quantifying such differences. Therefore, in order to extend the domain of perception and make it capable of quantifying a difference, it is essential to simultaneously present a known difference to the perception system. To reduce the probable errors involved in such assessments, it would be advisable not only to increase the number of known interrelated differences, but also to cover the range of unknown differences in small gradations, i.e., a series of related differences we call a designed scale.

Additionally, it is proven that the most understandable perceptible difference for the perception system is the lightness difference. (33-35) It is now possible to define such a designed common lightness scale in terms of any of the CIE recommended color difference equations, and quantify differences in any attribute in terms of such color difference equations. For instance, differences in specular gloss can be quantified not in terms of differences in gloss unit ([DELTA]GU), but in terms of a color difference unit ([DELTA]E). In this way, all geometric appearance attributes, or any other appearance attribute inclusive of chromatic or even futuristically defined attributes, could also be quantified in terms of this same designed common scale of achromatic differences. Provisions are now made for addition of geometric attributes together, or the addition of such geometric attributes, to chromatic, or any other attribute in order to define a total appearance index. Furthermore, tolerancing would easily be facilitated and defined in terms of color differences of the designed common lightness scale.

The common lightness scale samples were prepared in such a way that the color differences between the standard (sample 1) (i.e., the darkest sample) and each of the other samples (i.e., samples 2-8) were essentially only a lightness difference. Color differences between each common lightness scale sample (i.e., 2-8) and the standard (1) were calculated using the CIE 1976 color difference equation ([DELTA][E.sub.CIE 1976]), the color coordinates being obtained for the CIE D65 illuminant/1964 standard observer combination. Variation of color differences ([DELTA][E.sub.CIE 1976]) with the corresponding common lightness scale numbers (LS) are illustrated in Fig. 1.

As illustrated in Fig. 1, the lightness scale numbers (LS) are nonlinearly related to the actual lightness differences ([DELTA][E.sub.CIE 1976])

The designed common lightness scale was employed to visually quantify various geometric appearance attributes namely specular gloss, DOI, and OP, in terms of a color difference equation (i.e., [DELTA][E.sub.CIE 1976]). All visual assessment experiments were performed using a VeriVide CAC 120 light cabinet in a dark room, under a standard light source simulating D65. A gray painted inclined table was placed in the light cabinet to support the samples which were viewed at a distance of 50 cm subtending an angle of approximately 10[degrees] in the observer's eye. Sixteen observers including 9 males and 7 females with normal color visions (pre-tested by the Ishihara test) participated in the visual assessment experiments. Eight of the observers were experts in the field of appearance evaluations (i.e., automotive auditors), while the others were not experts.

A standard was selected visually having the highest values of specular gloss and DOI in the Specular Gloss scale or the DOI scale, respectively, and the lowest value of OP for the OP scale. In order to carry out, separately, the visual assessment experiments for each individual scale, the observers had the task first to perform a pairwise comparison and select the sample, in each pair, with the highest perceived specular gloss and DOI and the lowest OP in the corresponding scale, in effect, performing an indirect ordinal ranking. The second task was to evaluate the differences between the selected standard and each sample of each scale in terms of differences in lightness of the designed common lightness scale. Where possible, intermediate values were also given by many observers.

The visual assessment experiment for each observer was conducted in three separate sessions in each of which only one geometric attribute (e.g., specular gloss) was assessed for all samples constituting that individual scale. Each session was completed without any time restrictions. Time required to finish a session varied between 30 and 45 min.

In order to evaluate the correlation between differences in various instrumental parameters and the corresponding visually assessed differences, four statistical parameters namely, coefficient of determination ([R.sub.2]), gamma ([gamma]), coefficient of variation (CV), and standard residual sum of squares (STRESS) were used. These four statistical parameters can be calculated by the aid of equations (1a)-(1d) (36-37) for two seemingly equivalent data sets, namely [DELTA][E.sub.v] (i.e., visually quantified differences) and [DELTA]I (i.e., instrumentally measured differences).

In these equations, n is the number of samples; and F is an adjusting factor ensuring that on average [DELTA][E.sub.v] and [DELTA]I are equal. Near to 1 values of [R.sup.2] and [gamma] and near to zero values of CV and STRESS indicate close to best agreement between two sets of data (i.e., close to best agreement between visually perceived differences and instrumentally measured differences).

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII], (1a)

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII], (1b)

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII], (1c)

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII], (1d)

Results and discussion

The visually perceived lightness differences of the designed common lightness scale (LS values) were converted to the corresponding color differences [DELTA][E.sub.v] (the subscript "v" referring to "visual equivalence"). The calculated [DELTA][E.sub.v] values for the Specular Gloss, the DOI and the OP scales at each achromatic level are depicted in Table 4. For each sample, the average value of color difference obtained by all observers is reported.

The corresponding instrumentally measured differences ([DELTA]I) were also calculated for correlation purposes. It must be mentioned that our previous work (28) demonstrated that the overall inter-observer agreement was good (STRESS values less than 20%). Moreover, analyzing the scatter diagrams of visual differences ([DELTA][E.sub.v]) against the corresponding instrumental differences ([DELTA]I) for the Specular Gloss, the DOI, and the OP scales at four achromatic levels revealed that the [DELTA][E.sub.v] values for visually perceived specular gloss, DOI, and OP, and the equivalent [DELTA]I values for instrumentally measured parameters (i.e., specular gloss, DOI, and the parameter LW of the Wave scan instrument, respectively) correlated very well ([R.sup.2] > 0.9, STRESS < 18%). (28)

Having obtained such high correlations, and considering the fact that all the 12 scales were quantified on the basis of the same common lightness scale, visual differences (i.e., [DELTA][E.suv.v,G], [DELTA][E.sub.v,DOI], and [DELTA][E.sub.v,OP]) and the corresponding instrumental differences (i.e., [DELTA][G.sub.i], [DELTA][DOI.sub.i] and [DELTA][LW.sub.i]) were employed in order to determine the errors involved in implementation of the "principle of additivity." Equations (2) and (3) were used to calculate the "total visual difference" ([DELTA][E.sub.v,T]) values and the corresponding "total instrumental difference" ([DELTA][I.sub.T]) values, respectively, for each level of achromaticity, and for each group of observers (i.e., expert, inexpert, and all observers).

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII], (2)

where [DELTA][E.sub.v,G], [DELTA][E.sub.v,DOI], and [DELTA]{E.sub.v,OP] are visual differences in specular gloss, DOI, and OP, respectively.

[DELTA][I.sub.T] = 1/3 ([DELTA]G + [DELTA]DOI + [DELTA]LW). (3)

Similarly, [DELTA]G, [DELTA]DOI, and [DELTA]LW are calculated differences in instrumentally measured specular gloss, DOI, and LW parameters. Table 5 illustrates the implementation of the principle of additivity for both total visual differences ([DELTA][E.sub.v,T]) and total instrumental differences ([DELTA][I.sub.T]) at four levels of achromaticity.

Scatter diagrams of total visual differences ([DELTA][E.sub.v,T]) against the corresponding total instrumental differences ([DELTA][I.sub.T]) for three groups of observers are depicted in Fig. 2.

Correlation analyses using four statistical parameters, namely [R.sup.2], [gamma], CV, and STRESS values, given on the right hand side of each scatter plot, illustrate that high linear correlations exist between the total visual differences ([DELTA][E.sub.v,T]) and the corresponding calculated total instrumental differences ([DELTA][I.sub.T]) at all four achromatic levels, and for all three groups of observers (i.e., [R.sub.2] > 0.9 and STRESS < 20%). In other words, quantifying differences in geometric appearance attributes in terms of the same common lightness scale makes provisions for the summation of differences in each individual geometric appearance attribute to attain the total geometric appearance differences instrumentally with high correlations with the corresponding total visual differences.

Additionally, the error estimation data reported in Fig. 2 indicate that there seems to be a general good agreement between all observers. Average STRESS values of 11% for inexpert observers, 9% for expert observers, and 10% for all observers surprisingly show that there is no statistical difference between expert and inexpert observers in assessing total visual differences of geometric attributes. This demonstrates that simultaneous presentation of a known difference (i.e., a lightness difference in our case) to observers makes it much easier for them to quantify geometric attributes presented to them.

As mentioned previously, amongst all Wave scan parameters, the LW parameter was found to be the best quantitative representation of the visually perceived OP. However, a sample having the highest values of specular gloss and DOI has most likely the lowest LW value (i.e., OP) and vice versa. In order to adjust the LW parameter to the same numerical and ordinal scale as specular gloss and DOI (i.e., 100 for the best and 0 for the worst of that attribute), a new parameter we chose to call "absence of orange peel" (AOP) was introduced in this work to represent "percent absence of orange peel." To define such a parameter, the instrumentally measured LW values and the visual scale numbers of a purchased standard OP scale, namely the ACT visual OP standard [Advanced Coatings Technology (ACT), USA], were plotted and then normalized in such a way that, just like specular gloss and DOI, the AOP also gave values between zero and 100 representing the percent absence of orange peel. Equations (4a)-(4d) represent exponential, linear, quadratic, and cubic polynomial models as potential candidates to verify the best relationship existing between the visual scale numbers of the ACT standard orange peel scale ([ACT.sub.v]) and the corresponding instrumentally measured LW:

[ACT.sub.v] = a exp(-b(LW)), (4a)

[ACT.sub.v] = a(LW) + b, (4b)

[ACT.sub.v] = a[(LW).sup.2] + b(LW) + c, (4c)

[ACT.sub.v] = a[(LW).sup.3] + b[(LW).sup.2] + c(LW) + d. (4d)

Figure 3 illustrates such relationships together with the corresponding correlation errors.

Among these various models, the linear model has the lowest correlation coefficient and the highest [gamma], CV, and STRESS values and therefore was considered to be an inappropriate model. Although it is expected that high-degree polynomials would give even better correlations than the quadratic and the cubic relationships, but the simple exponential model giving [R.sub.2] = 0.98, [gamma] = 1.2, CV = 7.77, and STRESS = 6.82 would suffice for our purpose and was therefore chosen to represent the AOP derived from the Wave scan LW parameter, as shown in equation (5):

AOP = 100exp(-0.3LW). (5)

Just like specular gloss and DOI, the AOP parameter was normalized in such a way that its highest value is 100 for a sample having the lowest level of OP (i.e., theoretical zero value of LW). Such a parameter can now be suitably utilized to determine the lack of OP of any surface coating.

Having proved the intrinsic additivity property of the presently proposed visual assessment technique, the following general equation (equation (6)) was proposed to define a "total geometric appearance index" (GAI), as a combinatorial index of geometric appearance attributes, namely specular gloss (G), DOI, and OP for achromatic automotive finishes:

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII]. (6)

The subscript "st" in equation (6) refers to three standard samples having the highest value of these geometric attributes capable of being obtained at any particular factory (i.e., the highest attainable values for specular gloss (G), DOI, and %AOP. It is clear that the highest theoretical value for each attribute is 100%. Also, the subscript "s" refers to any production sample for which the GAI is required to be calculated; n is the number of geometric attributes under investigation, which in our case is 3; and [k.sub.1], [k.sub.2], and [k.sub.3] are coefficients to illustrate the contribution of each attribute to the total GAI. Analyzing the correlations between the total visual differences of all attributes and the corresponding total differences of instrumental values resulted in a linear relationship where [k.sub.1] = [k.sub.2] = [k.sub.3] = 1. Nearer values of GAI to 100 indicate near to perfect perceived appearances. The GAI values, calculated using equation (6), for various total visual differences at each of the four achromatic levels, are depicted in Table 6.

Plots of the total visual differences ([DELTA][E.sub.v,T]) vs the corresponding GAI values of the proposed derived GAI for each achromatic level and a combination of them all is depicted in Fig. 4.

Figure 4 clearly illustrates that there is a very high correlation between the proposed derived GAI values and the corresponding total visual equivalence for each and a combination of the four investigated achromatic levels. In other words, the principle of additivity holds good for specular gloss, DOI, and OP attributes, based on the same common lightness scale.

Having derived an unequivocally good GAI, the next tangible preliminary investigation was to determine the discrete acceptable and unacceptable tolerance regions for each and a combination of the three geometric attributes (i.e., specular gloss, DOI, and OP) at all levels of achromaticity.

In order to reach a preliminary determination of these tolerance regions, the same panel of observers carried out visual assessments on a pass/fail basis. The samples of the Specular Gloss, the DOI, and the OP scales were ordinally ranked, in the light cabinet, from best to worst appearance, according to visual difference values ([DELTA][E.sub.v]) they had previously obtained. The observers were then asked to select the sample in each series after which the rest of the samples had unacceptable total appearance. This evaluation was carried out, separately, for each achromatic level. The results of setting acceptable tolerances on each scale at four achromatic levels as well as the corresponding visual differences ([DELTA][E.sub.v]) are illustrated in Fig. 5. Figure 5 marks samples with acceptable total appearance with green solid circles, while the unaccepted samples are marked with red solid triangles.

Such tolerance regions were employed to set acceptable tolerances for the geometric attributes in terms of color differences ([DELTA]E) which are far more familiar to color and appearance specialists. As is seen in Fig. 5, for a sample having acceptable total appearance, the corresponding visual difference in terms of [DELTA][E.sub.CIE] 1976 is almost always below 3 (i.e., [DELTA][E.sub.v] < 3). The instrumental equivalents of specular gloss (G), DOI, AOP and the proposed GAI corresponding to the acceptable color difference (i.e., [DELTA][E.sub.v] < 3) were determined to be around 80, 85, 85, and 90, respectively, below which the appearance is visually unacceptable. Additionally, based on such color difference tolerancing, the permissible value of the instrumentally measured LW parameter would approximately be a value of 15.

Conclusions

The results reveal that the use of a common lightness scale has essentially alleviated the process of perception in technical visual assessments. In so doing, it has made provisions for effortless quantification of visual differences of geometric attributes with minimum errors and enhanced accuracy in the perceptibility of observers. Such quantifications were therefore attainable with ease, minimum errors, and ensured high correlation with instrumentally measured equivalences.

Such high correlations made provisions for the implementation of the principle of additivity which has in turn enhanced the possibility of deriving a GAI. However, before derivation of such an index, one measured parameter was remodeled exponentially to define a new parameter known as "percent absence of orange peel."

The newly derived GAI showed high correlations with the corresponding visual equivalents. Therefore, this novel index is recommended to be used as a standalone index for predicting the geometric appearance of automotive finishes. Additionally, the proposed GAI together with a chromatic appearance index have the potential of being utilized as a foundation for deriving a total appearance index for the automotive industry.

DOI 10.1007/s11998-014-9608-5

Acknowledgment The authors wish to thank the Iran Khodro car manufacturing company as well as the Center of Excellence for Color Science and Technology for their support.

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F. Mirjalili, S. Moradian

Department of Polymer Engineering and Color Technology, Amirkabir University of Technology, P.O. Box 15875-4413, Tehran, Iran

F. Ameri ([mail])

Department of Color Physics, Institute for Color Science and Technology. P.O. Box 16765-654, Tehran. Iran

e-mail: fameri@icrc.ac.ir

Table 1: Instrumental data for the Specular Gloss (G) scale at four
achromatic levels

Sample            Specular gloss   DOI    LW

Metallic black
  GB1 (a)              90.9        98.1   8
  GB2                  86.8        98.2   7.3
  GB3                  80          96.6   8.6
  GB4                  70.4        92.2   8.2
  GB5                  66          92.8   6.7

Metallic silver
  GS1 (a)              91.6        96.8   3.1
  GS2                  89.7        96.3   2.1
  GS3                  84          93.5   3
  GS4                  74.9        90.7   2.6
  GS5                  65.8        89     2.7

Metallic gray
  GG1 (a)              89.3        99.2   3.3
  GG2                  84.8        98     2.7
  GG3                  80.1        96.9   3.5
  GG4                  73.5        95     3.4
  GG5                  67.6        92.6   3.8

Solid white
  GW1 (a)              89.8        99.1   2.8
  GW2                  84.9        97.5   3
  GW3                  81.6        96.5   3
  GW4                  74.1        94     4.4
  GW5                  66          91.1   4

(a) The first sample in each series is the standard panel

Table 2: Instrumental data for the DOI scale at four achromatic
levels

Sample            Specular gloss   DOI    LW

Metallic black
  DB1 (a)              89.8        98.7   5.5
  DB2                  87.9        98     6.7
  DB3                  85.5        85.8   5.6
  DB4                  82          71.3   5.5
  DB5                  80.9        63.5   6.4

Metallic silver
  DS1 (a)              90.8        93.6   3.5
  DS2                  89.5        91.8   2.5
  DS3                  89          83.9   2.9
  DS4                  918         78     4
  DS5                  91.4        75.3   3.9
  DS6                  87.6        61.1   5.3

Metallic gray
  DG1 (a)              88.3        97.8   4.1
  DG2                  88.3        95.8   5.1
  DG3                  88.8        94.7   3.4
  DG4                  87.7        85     4
  DG5                  84.7        72     4.2
  DG6                  85.2        64.6   6.6

Solid white
  DW1 (a)              88.2        93.7   4.9
  DW2                  91.4        89     4.1
  DW3                  87.3        83.1   5.1
  DW4                  86.3        78     5.2
  DW5                  84.2        68.6   4.5

(a) The first sample in each series is the standard panel

Table 3: Instrumental data for the Orange Peel (OP) scale at four
achromatic levels

Sample            Specular gloss   DOI     LW

Metallic black
  OPB1 (a)             91.7        98.5   4.1
  OPB2                 85.1        98.6   9.1
  OPB3                 83.6        96.1   12.3
  OPB4                 88.9        94.7   16
  OPB5                 88.3        95.4   19.6
  OPB6                 89          94.9   26.8
  OPB7                 92.3        91.1   32.5
  OPB8                 88.2        91     52.1

Metallic silver
  OPS1 (a)             91.8        95.5   2.4
  OPS2                 91.6        90.4   4.6
  OPS3                 91.8        95.5   10.6
  OPS4                 90.3        87.1   15.4
  OPS5                 91.4        88.9   22.8
  OPS6                 85.2        83.4   26
  OPS7                 89.1        85.7   31.8
  OPS8                 87          85.4   51

Metallic gray
  OPG1 (a)             87.1        98.2   3
  OPG2                 87.4        96.5   4.1
  OPG3                 88.3        96     9.7
  OPG4                 84.6        93.5   15.6
  OPG5                 84.4        90.2   20.3
  OPG6                 88.4        93.3   24.5
  OPG7                 84.2        81.5   35.7
  OPG8                 86.6        86.6   43.3

Solid white
  OPW1 (a)             84.9        98.6   2.4
  OPW2                 87.5        99.2   2.9
  OPW3                 83          98.1   5.2
  OPW4                 89.6        97.9   9.9
  OPW5                 88.5        97.9   16.1
  OPW6                 89.6        91.8   23.3
  OPW7                 89.3        89.8   34
  OPW8                 88          89.2   53.9

(a) The first sample in each series is the standard panel

Table 4: Visual data in terms of AEV for the Specular Gloss (G), the
DOI, and the Orange Peel (OP) scales at four achromatic levels

Metallic black                            Metallic gray

Sample       [DELTA] [E.sub.v]   Sample       [DELTA] [E.sub.v]

Specular Gloss scale (G)
  GB1 (a)          0             GG1 (a)            0
  GB2              0.87          GG2                2.47
  GB3              2.82          GG3                2.11
  GB4              4.29          GG4                4.44
  GB5              7.07          GG5                5.43

DOI scale
  DB1 (a)          0             DG1 (a)            0
  DB2              1.21          DG2                1.97
  DB3              4.09          DG3                1.07
  DB4              6.38          DG4                4.37
  DB5              7.98          DG5                5.88
                                 DG6                9.45

Orange Peel scale (OP)
  OPB1 (a)         0             OPG1 (a)           0
  OPB2             1.96          OPG2               1.19
  OPB3             3.24          OPG3               2.48
  OPB4             4.27          OPG4               3.49
  OPB5             4.71          OPG5               5.26
  OPB6             6.44          OPG6               5.72
  OPB7             7.00          OPG7               7.65
  OPB8             9.25          OPG8               12.76

Metallic silver                           Solid white

Sample       [DELTA] [E.sub.v]   Sample       [DELTA] [E.sub.v]

Specular Gloss scale (G)
  GS1 (a)          0             GW1 (a)            0
  GS2              1.35          GW2                1.82
  GS3              2.87          GW3                1.98
  GS4              4.75          GW4                4.30
  GS5              6.70          GW5                6.03

DOI scale
  DS1 (a)          0             DW1 (a)            0
  DS2              1.09          DW2                2.69
  DS3              2.41          DW3                2.77
  DS4              3.13          DW4                5.02
  DS5              6.49          DW5                6.77
  DS6              8.02

Orange Peel scale (OP)
  OPS1 (a)         0             OPW1 (a)           0
  OPS2             1.57          OPW2               1.14
  OPS3             1.43          OPW3               1.58
  OPS4             2.37          OPW4               2.23
  OPS5             5.11          OPW5               3.06
  OPS6             3.85          OPW6               4.29
  OPS7             5.28          OPW7               7.22
  OPS8             10.07         OPW8               9.78

(a) The first sample in each set is the standard panel

Table 5: Total visual differences ([DELTA] [E.sub.v, T]) as well as
the corresponding total instrumental differences ([DELTA]/sub.T]) for
different groups of observers at four achromatic levels

[DELTA] [E.sub.v, T]                                    [DELTA]/sub.T]

Expert observers   Inexpert observers   All observers

Metallic black
0                  0                    0               0
1.45               1.24                 1.34            3.27
3.69               4.05                 3.87            13.11
5.39               6.01                 5.70            23.53
7.75               6.95                 7.35            29.51

Metallic silver
0                  0                    0               0
1.35               1.38                 1.37            1.95
2.63               2.52                 2.58            10.08
3.82               4.04                 3.93            18.62
6.52               5.92                 6.22            24.47

[DELTA] [E.sub.v, T]                                    [DELTA]/sub.T]

Expert observers   Inexpert observers   All observers

Metallic gray
0                  0                    0               0
1.88               2.12                 2               4.77
3.27               3.38                 3.32            11.54
5.41               5.28                 5.34            21.01
8.26               6.76                 7.51            29.21

Solid white
0                  0                    0               0
1.92               2.62                 2.27            5.69
3.12               2.15                 2.63            10.84
4.53               4.58                 4.56            17.44
7.23               6.29                 6.76            26.84

Table 6: GAI values for various total visual differences
([DELTA][E.sub.v,T]) at four achromatic levels

Metallic black              Metallic gray

[DELTA][E.sub.v,T]   GAI    [DELTA][E.sub.v,T]   GAI

0                    100    0                    100
1.18                 95.1   1.39                 95.4
1.34                 90.9   2.00                 90.0
3.38                 86.9   3.32                 85.0
5.12                 79.8   5.34                 78.4
7.35                 73.4   7.51                 72.9

Metallic silver             Solid white

[DELTA][E.sub.v,T]   GAI    [DELTA][E.sub.v,T]   GAI

0                    100    0                    100
1.15                 95.8   1.11                 95.4
1.37                 93.9   2.03                 89.4
2.58                 85.5   2.35                 84.8
3.93                 78.8   4.16                 79.8
6.22                 75.2   6.76                 73.7


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Author:Mirjalili, F.; Moradian, S.; Ameri, F.
Publication:Journal of Coatings Technology and Research
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Date:Nov 1, 2014
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