Color prediction method for mixed-color cotton fibers

Abstract

The invention provides a mixed-color cotton fiber color prediction method, and relates to the technical field of mixed-color cotton fiber color prediction, and the method comprises the steps: obtaining the chromaticity parameter of a single-color cotton fiber; according to the chromaticity parameters of the single-color cotton fibers and a preset Stearns-Noechel model, calculating the spectral reflectivity of the sample after color mixing of the colored cotton fibers; calculating a color value after color mixing according to the spectral reflectivity of the sample after color mixing of the colored cotton fibers; the invention provides a novel color prediction method for mixed-color cotton fibers. The method has the advantages of high detection precision, simplicity and convenience in operation, low cost, high efficiency and the like, and is suitable for large-scale rapid color detection and matching.

Description

Technical Field

[0001] The invention relates to the technical field of color prediction of mixed-color cotton fibers, and in particular to a color prediction method for mixed-color cotton fibers. Background Art

[0002] Color-spun production involves the simple physical mixing of fibers of different colors in specific proportions. As a promising product in the textile industry, it boasts environmental friendliness, low energy consumption, minimal pollution, shortened production processes, and a strong sense of three-dimensional color. Color matching for color-spun yarns is primarily categorized as manual and computer-generated. Currently, the biggest challenge with color-spun products is suboptimal color matching, making them unable to adapt to the "small batch, high variety, and rapid update" requirements of color-spun products. This has severely restricted their development, leading to the development of computer-generated color matching models, which primarily utilize modern colorimetry theory. Fiber blending models study the color mapping relationship between colored and blended fibers. Three commonly used color-mixing models are the Friele model, the Stearns-Noechel model, and the Kubelka-Munk theory. The Stearns-Noechel model is more suitable for bulk fiber color matching studies.

[0003] In the existing technology, first, an adaptive weighted particle swarm algorithm is used to predict the dye formula of wood, and it is found that when the M value is a fixed value of 0.25, the color difference between the dyed fitting sample and the standard sample is the smallest; second, three colors of fibers are used to explore the color prediction model of ring-spun blended yarn, and it is found that the prediction effect of the M value calculated based on wavelength is the best; third, a wool blended yarn color prediction model is constructed based on the BP neural network; fourth, a two-band improved color prediction model; there are two problems in the above technologies. First, the importance of color measurement is ignored, and the accuracy of the color matching model depends on the accurate color measurement method. Second, in order to ensure the universality of the color prediction model, the color of the sample should be rich enough; therefore, the accuracy of the color prediction method in the existing technology is low. Summary of the Invention

[0004] In view of the above technical problems, the technical solution adopted by the present invention is:

[0005] According to a first aspect of the present application, a method for predicting the color of mixed-color cotton fibers is provided, the method comprising the following steps:

[0006] S100, obtaining chromaticity parameters of single-color cotton fibers;

[0007] S200, calculates the spectral reflectance of the sample after the colored cotton fibers are mixed based on the chromaticity parameters of the single-color cotton fibers and the preset Stearns-Noechel model;

[0008] S300: Calculate the color value of the mixed color according to the spectral reflectance of the sample after the colored cotton fiber is mixed.

[0009] Furthermore, step S100 includes the following steps:

[0010] S110, weighing a preset mass of single-color cotton fiber and placing it into a test container; the test container is a cylindrical plastic box with an openable lid;

[0011] S120, using a calibrated spectrophotometer, record the spectral reflectance R of the monochromatic cotton fiber in the range of 360nm to 720nm under the conditions of D65 light source, 10° observation angle, and 100% UV. i (λ), the value is taken at intervals of 10 nm; R i (λ) represents the spectral reflectance of the monochromatic fiber of the i-th component at wavelength λ.

[0012] Furthermore, the test container has a height of 5.5 cm and a bottom diameter of 6 cm; the compression density is about 160 kg / m 3 .

[0013] Furthermore, step S200 includes the following steps:

[0014] S210, according to the M value formula, obtain the M value of the sample wavelength λ after color mixing between 360 and 720 nm with an interval of 10 nm; wherein the M value formula conforms to the following relationship:

[0015]

[0016] S220, substitute the M value into the Stearns-Noechel model to obtain the spectral reflectance f[R i (λ)]; the Stearns-Noechel model conforms to the following relationship:

[0017]

[0018] Among them, the Stearns-Noechel model is the relationship between the spectral reflectance of the sample obtained by mixing and the spectral reflectance of the monochromatic fiber f[R(λ)] represents the spectral reflectance of the mixed color fiber when the wavelength is λ; C i represents the mixing ratio of single-color fibers in the i-th component; R i (λ) represents the spectral reflectance of the monochromatic fiber of the i-th component at wavelength λ.

[0019] Furthermore, step S300 includes the following steps:

[0020] S310, according to the formulas of ideal tristimulus values X, Y, and Z, and with the help of conversion formulas of various standard color spaces in colorimetry theory, obtain color values in RGB, Lab, and Lch color spaces; wherein the formulas of X, Y, and Z are as follows:

[0021]

[0022] E(λ) represents the spectral characteristics of the light source; R(λ) represents the reflectance spectrum measured by the spectrophotometer; and They represent the three stimulus value functions of the human eye specified by CIE; k is the adjustment factor for adjusting the Y value of the light source to 100.

[0023] The present invention has at least the following beneficial effects:

[0024] The color prediction method of mixed-color cotton fibers of the present invention studies the cotton fiber color mixing algorithm based on the Stearns-Noechel model. First, the influence of compression density on the color measurement of bulk fibers is explored, thereby determining the color measurement conditions suitable for cotton fibers. Then, red, yellow, blue, black, and white fibers are used as raw materials, and two-component mixed-color samples and three-component mixed-color samples are set according to different gram weight gradients. They are fully mixed with the help of a carding machine, and their spectral reflectance and chromaticity parameters are measured. The Stearns-Noechel model is used to calculate the undetermined parameter values of each group of mixed-color samples, and the intrinsic relationship between the key parameter M and the mixed-color sample, the number of mixed-color samples, the wavelength, etc. is explored, thereby constructing a color prediction method for colored cotton fibers after color mixing. The present invention provides a new color prediction method for mixed-color cotton fibers. The method has the advantages of high detection accuracy, simple operation, low cost, high efficiency, etc., and is suitable for large-scale rapid color measurement and matching. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0026] Figure 1 A flow chart of a method for predicting the color of mixed-color cotton fibers provided in an embodiment of the present invention;

[0027] Figure 2 A flowchart of the cotton fiber color measurement conditions provided by the embodiment of the present invention;

[0028] Figure 3 A schematic diagram showing the effect of compression density on L color value provided by an embodiment of the present invention;

[0029] Figure 4 A schematic diagram showing the effect of compression density on the color value of a provided in an embodiment of the present invention;

[0030] Figure 5 A schematic diagram showing the effect of compression density on b color value provided by an embodiment of the present invention;

[0031] Figure 6 A schematic diagram comparing the color difference values of five single-color cotton fibers before and after compression density changes provided by an embodiment of the present invention;

[0032] Figure 7 A schematic diagram of M values at different wavelengths provided by an embodiment of the present invention;

[0033] Figure 8 A schematic diagram of standard deviation at different wavelengths provided by an embodiment of the present invention;

[0034] Figure 9 A schematic diagram of average M values at different wavelengths provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0035] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without making any creative efforts shall fall within the scope of protection of the present invention.

[0036] It should be noted that, based on this disclosure, those skilled in the art will appreciate that an aspect described herein can be implemented independently of any other aspect, and that two or more of these aspects can be combined in various ways. For example, any number of the aspects described herein can be used to implement an apparatus and / or practice a method. In addition, other structures and / or functionalities other than one or more of the aspects described herein can be used to implement such an apparatus and / or practice such a method.

[0037] The following will refer to Figure 1 The flowchart of the color prediction method of mixed-color cotton fiber shown is used to introduce a color prediction method of mixed-color cotton fiber.

[0038] In this implementation, the color measurement method of cotton fiber was first determined, and then the spectral reflectance of the single-color cotton fiber was obtained by using a spectrophotometer test. The color value of the cotton fiber after mixing was predicted based on the wavelength optimization method of the key parameter M value.

[0039] The color prediction method of the mixed color cotton fiber may comprise the following steps:

[0040] S100, obtaining chromaticity parameters of single-color cotton fibers.

[0041] Furthermore, step S100 includes the following steps:

[0042] S110, weighing a preset mass of single-color cotton fiber and placing it into a test container; the test container is a cylindrical plastic box with an openable lid.

[0043] S120, using a calibrated spectrophotometer, record the spectral reflectance R of the monochromatic cotton fiber in the range of 360nm to 720nm under the conditions of D65 light source, 10° observation angle, and 100% UV. i (λ), the value is taken at intervals of 10 nm; R i (λ) represents the spectral reflectance of the monochromatic fiber of the i-th component at wavelength λ.

[0044] In this example, 20g of single-color cotton fiber can be weighed and placed in a test container. The container is a removable cylindrical plastic box (5.5cm high, 6cm bottom diameter), with a compressed density of approximately 160kg / m³. The lid can be opened to insert a desired amount of fiber. A circular hole is opened in the center of the bottom of the container, slightly larger than the test aperture of the spectrophotometer, to eliminate the influence of the container's color on the color measurement results.

[0045] After the spectrophotometer is turned on and calibrated, the spectral reflectance value R of the sample in the range of 360nm to 720nm is recorded under the conditions of D65 light source, 10° observation angle, and 100% UV. i (λ), the value is taken at intervals of 10 nm.

[0046] S200 calculates the spectral reflectance of a sample after the colored cotton fibers are mixed based on the chromaticity parameters of the single-color cotton fibers and a preset Stearns-Noechel model.

[0047] Furthermore, step S200 includes the following steps:

[0048] S210, according to the M value formula, obtain the M value of the sample wavelength λ after color mixing between 360 and 720 nm with an interval of 10 nm; wherein the M value formula conforms to the following relationship:

[0049]

[0050] S220, substitute the M value into the Stearns-Noechel model to obtain the spectral reflectance f[R i (λ)]; the Stearns-Noechel model conforms to the following relationship:

[0051]

[0052] Among them, the Stearns-Noechel model is the relationship between the spectral reflectance of the sample obtained by mixing and the spectral reflectance of the monochromatic fiber f[R(λ)] represents the spectral reflectance of the mixed color fiber when the wavelength is λ; C i represents the mixing ratio of single-color fibers in the i-th component; R i (λ) represents the spectral reflectance of the monochromatic fiber of the i-th component at wavelength λ.

[0053] In this embodiment, theoretically, fiber color mixing is a physical mixing, which is to mix fibers of different colors to form a special color. The relationship between the spectral reflectance of the sample obtained after mixing and the spectral reflectance of the monochromatic fiber is shown in formula (1).

[0054]

[0055] In formula (1), f[R(λ)] represents the spectral reflectance of the mixed color fiber at wavelength λ; C i represents the mixing ratio of single-color fibers in the i-th component; R i (λ) represents the spectral reflectance of the monochromatic fiber of the i-th component at wavelength λ.

[0056] In order to better explain the relationship between the colors of colored fibers before and after mixing, Stearns and Noechel introduced the undetermined parameter M based on the above formula, and proposed an empirical formula based on the experimental results, namely the Stearns-Noechel model as shown in formula (2):

[0057]

[0058] In formula (2), M is an undetermined parameter, which is related to the optical properties, color mixing ratio, length, type and fineness of the fiber.

[0059] The method of optimizing the M value by wavelength is shown in formula (3), where the first three segments are linearly fitted and the last segment adopts a fixed value.

[0060]

[0061] According to formula (3), the M value with λ between 360nm and 720nm and interval of 10nm can be obtained. Then, the M value is substituted into formula (2) to obtain the spectral reflectance R of the i-th component fiber. i (λ), according to the color mixing ratio C i Formula (1) can be used to calculate the spectral reflectance R(λ) of the sample after color mixing at wavelengths between 360 nm and 720 nm.

[0062] S300: Calculate the color value of the mixed color according to the spectral reflectance of the sample after the colored cotton fiber is mixed.

[0063] Furthermore, step S300 includes the following steps:

[0064] S310, according to the formulas of ideal tristimulus values X, Y, and Z, and with the help of conversion formulas of various standard color spaces in colorimetry theory, obtain color values in RGB, Lab, and Lch color spaces; wherein the formulas of X, Y, and Z are as follows:

[0065]

[0066] E(λ) represents the spectral characteristics of the light source; R(λ) represents the reflectance spectrum measured by the spectrophotometer; and They represent the three stimulus value functions of the human eye specified by CIE; k is the adjustment factor for adjusting the Y value of the light source to 100.

[0067] The principle of the method in this embodiment is explained as follows:

[0068] (1) Color testing method for cotton fibers

[0069] The premise of accurate color matching is accurate color measurement results. Fiber is a translucent material. When the thickness is insufficient, the base color of the container holding the fiber will affect the color measurement results. Secondly, the fluffy fiber aggregate shows the color of the mixture of fiber and air. Therefore, when measuring the color of fibrous materials, they must be piled up to a sufficient thickness so that the container color does not leak, and the air between the fibers must be squeezed out under pressure to reflect the true color of the fiber. Therefore, we first explore the color measurement conditions suitable for cotton fibers. The idea is as follows Figure 2 As shown in the figure, when the container size is determined, the test variable is set to gram weight, that is, compressed density. The color measurement method suitable for cotton fiber is determined based on the principle that the color values L, a, and b of the sample are constant under the best color measurement conditions, that is, the color difference is as close to 0 as possible.

[0070] Given a given volume, the mass of loose fibers is directly proportional to their compressed density. Therefore, in a closed container, the effect of compressed density on color was analyzed by varying the weight of the fibers within. Using an FA1004N electronic balance, 10g, 15g, 20g, 25g, and 30g of red, yellow, blue, black, and white cotton fibers were weighed in sequence and placed into the container until the fibers filled the entire container.

[0071] The color value changes of five monochromatic fibers with different compression densities are as follows Figures 3 to 5As shown in the figure, it can be seen that when the weight increases from 10g to 25g, the color values of the five monochrome cotton fibers change significantly, especially the brightness L value changes more obviously. The L value of the five monochrome cotton fibers shows an overall increasing trend with the increase of compression density, and the light color changes more than the dark color; the chromaticity index a and b value change relatively little, a represents red and green, and with the increase of compression density, it has the greatest impact on red cotton fiber, and the other four monochrome cotton fibers have almost no change; b represents yellow and blue, and has the greatest impact on yellow cotton fiber, and the other four monochrome cotton fibers have almost no change. When the fiber weight increases from 25g to 30g, the tested color value Lab hardly changes, suggesting that 25g may be the best test condition.

[0072] Next, we will determine the best color measurement conditions for cotton fibers from the perspective of color difference. In textiles, the CMC (2:1) formula is generally used to express color difference. Figure 6 It shows the color difference values when the weight of five single-color cotton fibers is 10g and 15g, 15g and 20g, 20g and 25g, 25g and 30g. It can be seen that when the fiber weight changes from 25g to 30g, the color difference value is the smallest, and the color difference values of the five single-color samples are all less than 0.3, which is consistent with the above Figures 3 to 5 The analysis is consistent, therefore, this experiment will use 25g as the optimal test weight, and the compression density is about 160kg / m 3 .

[0073] (2) Determination of the key parameter M value in the Stearns-Noechel model

[0074] In order to explore whether the key parameter M value in the Stearns-Noechel model is related to the wavelength, since the M value of the same sample at different wavelengths is different, assuming that the predicted spectral reflectance after color mixing is the same as the actual spectral reflectance, the M value corresponding to each wavelength between 360 and 720 nm of the mixed color sample can be obtained. The results are as follows: Figure 7 As shown in the figure, the M values corresponding to most wavelengths are concentrated between 0 and 0.25, and the M values in the first half of the wavelength band are more discrete, while the M values corresponding to the second half of the wavelength band are relatively concentrated. When the wavelength is less than 500nm, the M values are mostly distributed between 0 and 0.5. When the wavelength is 500nm to 590nm, the M values have two distribution ranges, 0 to 0.2 and -0.1 to 0. When the wavelength is greater than 590nm, most M values are distributed between 0 and 0.25. Figure 8 Indicates the discreteness of the M value corresponding to different wavelengths. Figure 7 It can be seen that the M values in the first half are relatively dispersed, but numerical analysis shows that most standard deviations are less than 0.2, indicating that the differences are basically not significant.

[0075] from Figure 7 and Figure 8It can be seen that the mean values of M values at different wavelengths show a certain regularity and are relatively concentrated, so the mean values of M values at different wavelengths are calculated. The results are as follows: Figure 9 As shown in the figure, when the wavelength increases from 360nm to 620nm, the average M value fluctuates regularly. As the wavelength increases, the M value increases first, then decreases, and then increases again. When the wavelength exceeds 620nm, the average M value gathers around 0.13.

[0076] Therefore, the distribution curves of wavelength and M value can be divided into four fitting segments, of which the first three segments are linear fitting and the last segment adopts a fixed value. This method of calculating M value is called Model II, as shown in formula (4). The correlation R2 of the first three fitting curves are 0.67, 0.76 and 0.81 respectively.

[0077]

[0078] To verify the accuracy of using the wavelength-modified M-value to predict the color of mixed-color cotton fibers, known parameters were substituted into the predicted spectral reflectance of the corresponding mixed-color samples. The predicted values were then compared with the actual values using the CMC (2:1) color difference formula. The results showed that 33.7% of the samples had a color difference less than 1, 70.8% had a color difference less than 2, and 99.6% had a color difference less than 3, demonstrating the applicability of this method for predicting the color of mixed-color cotton fibers.

[0079] In a specific embodiment, the single-color cotton fibers include red cotton fibers and yellow cotton fibers, and the color mixing ratio is set to 1:1.

[0080] The spectral reflectance of red and yellow cotton fibers was tested using step 1, and the results are shown in Table 1. The spectral reflectance of the mixed color sample was calculated using step 2, and the results are shown in Table 2. The tristimulus values X, Y, and Z of the mixed color sample were obtained using step 3, and the results are shown in Table 3.

[0081] Table 1 Spectral reflectance of monochromatic fibers

[0082]

[0083] Table 2 Spectral reflectance of mixed color fibers

[0084]

[0085]

[0086] Table 3 Color values of mixed color samples

[0087]

[0088] The above results show that the color prediction method of mixed-color cotton fibers in this embodiment has a good effect.

[0089] The color prediction method of mixed-color cotton fibers in this embodiment studies the cotton fiber color mixing algorithm based on the Stearns-Noechel model. First, the influence of compression density on the color measurement of bulk fibers was explored, thereby determining the color measurement conditions suitable for cotton fibers. Then, red, yellow, blue, black, and white fibers were used as raw materials, and two-component mixed-color samples and three-component mixed-color samples were set according to different weight gradients. They were fully mixed with the help of a carding machine, and their reflectivity and chromaticity parameters were measured. The Stearns-Noechel model was used to calculate the undetermined parameter values of each group of mixed-color samples, and the intrinsic relationship between the key parameter M and the mixed-color samples, the number of mixed-color samples, the wavelength, etc. was explored, thereby constructing a color prediction method for colored cotton fibers after color mixing. The present invention provides a new color prediction method for mixed-color cotton fibers. This method has the advantages of high detection accuracy, simple operation, low cost, and high efficiency, and is suitable for large-scale rapid color matching.

[0090] Furthermore, although the steps of the method of the present disclosure are described in a particular order in the accompanying drawings, this does not require or imply that the steps must be performed in this particular order, or that all steps shown must be performed to achieve the desired results. Additionally or alternatively, some steps may be omitted, multiple steps may be combined into one step, and / or one step may be decomposed into multiple steps.

[0091] Although some specific embodiments of the present invention have been described in detail by way of examples, it should be understood by those skilled in the art that the above examples are for illustration only and are not intended to limit the scope of the present invention. It should also be understood by those skilled in the art that various modifications may be made to the embodiments without departing from the scope and spirit of the present invention.

Claims

1. A color prediction method for mixed-color cotton fibers, characterized in that: The method comprises the following steps: S100, obtaining chromaticity parameters of single-color cotton fibers; S200, calculates the spectral reflectance of the sample after the colored cotton fibers are mixed based on the chromaticity parameters of the single-color cotton fibers and the preset Stearns-Noechel model; S300: Calculate the color value of the mixed color according to the spectral reflectance of the sample after the colored cotton fiber is mixed.

2. The color prediction method of mixed color cotton fiber according to claim 1, characterized in that: Step S100 includes the following steps: S110, weighing a preset mass of single-color cotton fiber and placing it into a test container; the test container is a cylindrical plastic box with an openable lid; S120, using a calibrated spectrophotometer, record the spectral reflectance R of the monochromatic cotton fiber in the range of 360nm to 720nm under the conditions of D65 light source, 10° observation angle, and 100% UV. i (λ), the value is taken at intervals of 10 nm; R i (λ) represents the spectral reflectance of the monochromatic fiber of the i-th component at wavelength λ.

3. The color prediction method of mixed color cotton fiber according to claim 2, characterized in that: The test container has a height of 5.5 cm and a bottom diameter of 6 cm; the compression density is 160 kg / m 3 .

4. The color prediction method of mixed color cotton fiber according to claim 2, characterized in that: Step S200 includes the following steps: S210, according to the M value formula, obtain the M value of the sample wavelength λ after color mixing between 360 and 720 nm with an interval of 10 nm; wherein the M value formula conforms to the following relationship: S220, substitute the M value into the Stearns-Noechel model to obtain the spectral reflectance f[R i (λ)]; the Stearns-Noechel model conforms to the following relationship: Among them, the Stearns-Noechel model is the relationship between the spectral reflectance of the sample obtained by mixing and the spectral reflectance of the monochromatic fiber f[R(λ)] represents the spectral reflectance of the mixed color fiber when the wavelength is λ; C i represents the mixing ratio of single-color fibers in the i-th component; R i (λ) represents the spectral reflectance of the monochromatic fiber of the i-th component at wavelength λ.

5. The color prediction method of mixed color cotton fiber according to claim 4, characterized in that: Step S300 includes the following steps: S310, according to the formulas of ideal tristimulus values X, Y, and Z, and with the help of conversion formulas of various standard color spaces in colorimetry theory, obtain color values in RGB, Lab, and Lch color spaces; wherein the formulas of X, Y, and Z are as follows: E(λ) represents the spectral characteristics of the light source; R(λ) represents the reflectance spectrum measured by the spectrophotometer; and They represent the three stimulus value functions of the human eye specified by CIE; k is the adjustment factor for adjusting the Y value of the light source to 100.

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