Rapid color prediction method for colored textile fabric

Abstract

The invention discloses a rapid color prediction method for a colored fabric. The method comprises the following steps: step 1.1, constructing a color-non-color combined spectrum correction equation; step 1.2, constructing a fiber-fabric optical structure color transfer equation; and step 2, predicting the reflectivity from monochromatic fibers to mixed-color fabrics. According to the method, on the basis that only a small number of training samples are used, color prediction from single-color fibers to mixed-color fabrics (the method can be suitable for color prediction of color textiles composed of chemical fibers, wool, cotton, polyester and other fibers) can be achieved, and meanwhile the accuracy is far higher than that of an existing original color prediction model.

Description

[Technical field]

[0001] The present invention relates to the technical field of color prediction, in particular to the technical field of color prediction of colored woven fabrics. [Background technology]

[0002] Colored fabrics can be blended by using two or more fibers of different colors. Colored fabrics are popular among people because of their advantages such as rich colors, hazy colors and environmental protection. However, the color matching production of colored fabrics has long been plagued by problems such as color difference in sample making, long proofing cycle and large proofing quantity, which has caused color spinning companies to be troubled.

[0003] Color spinning enterprises can mainly use the Stearns-Noechel (SN) model, Friele model and Kubelka-Munk (KM) theory to achieve color prediction when matching the color of colored textiles; on this basis, many scholars have also conducted further research to try to improve the color prediction ability; in 2018, Wei Chunao et al. (Wei CA, Wan XX and Li JF. A modified single-constant Kubelka-Munk model for color prediction of pre-colored fiber blends. Cellulose 2018; 25: 2091-2102.) proposed a new empirical summation theory based on the single constant KM model and introduced a correction factor n for color prediction of colored textiles, and the prediction model can effectively improve the color prediction ability of the single constant KM model; in 2019, Liu Yang et al. (Liu Y, Jing B, Zhou RY, et al. The optimization of color-prediction models for colored cotton fiber yarns function. Text Res J 2019;89(19-20):4007-4014.) The selection of empirical parameters of the SN model and the Friele model was studied, and it was found that when the number of component types in the mixed fiber is less than 5, the SN model has a better prediction effect than the Friele model; however, since the above research only studied the single form of color-woven fabrics or their fibers, it ignored the color changes caused by the physical structure differences between fibers and fabrics. Therefore, it is difficult to apply to the actual production of color-woven fabrics, which makes the color matching of color-woven fabrics still very difficult.

[0004] At present, only a few scholars have conducted a small amount of regular research on the color relationship between fibers, yarns and fabrics; in 2019 and 2021, Yuan Li et al. (Yuan Li, Xiong Ying, Gu Qian et al. Color transfer law between dyed fibers and colored spun yarns and its influencing factors [J]. Journal of Textile Research, 2021, 42(05):122-129; Yuan Li, Wang Danshu, Gu Qian et al. Color law between colored spun yarns and fabrics based on spectral pan-similarity measurement [J]. Journal of Textile Research, 2019, 40(02):30-37.) established a spectral pan-similarity measurement model based on discrete Fréchet distance and Pearson correlation coefficient, and this model can be used to evaluate the spectral similarity between fibers, yarns and fabrics; in 2016, Li Changlei et al. (Li Changlei, Ma Junzhi, Qin Cuimei et al. Study on color law between colored viscose fibers, yarns and fabrics. Knitting Industry Industry 2016(11):47-51.) studied the relationship between monochrome viscose fiber and its yarn and fabric, and found that the reflectivity curves between fiber, yarn and fabric are consistent; in 2019, Jin Jiabing et al. and Cheng Lu et al. (Jin Jiabing, Guo Mingrui, Fu Jiajia, etc. Research on the relationship between yarn twist coefficient and brightness based on image processing [J]. Cotton Textile Technology, 2019, 47(09):18-21; Cheng Lu, Ge Mengjia, Cao Jiqiang, etc. Research on the influence of yarn number and twist coefficient on color difference of colored spun yarn [J]. Cotton Textile Technology, 2020, 48(04):25-29.) studied the influence of yarn twist coefficient on yarn color, and found that the larger the yarn twist coefficient, the lower the yarn brightness; however, these studies only analyzed the color change rules between fibers, yarns and fabrics, and did not conduct further research on it. [Summary of the invention]

[0005] The purpose of the present invention is to solve the problems in the prior art and to propose a fast color prediction method for colored fabrics. The present invention can realize color prediction from monochrome fibers to mixed-color fabrics (applicable to color prediction of colored fabrics composed of fibers such as chemical fibers, wool, cotton and polyester) based on only a small amount of training samples, and the accuracy is much higher than the existing original color prediction model.

[0006] To achieve the above object, the present invention proposes a rapid color prediction method for a yarn-dyed fabric, comprising the following steps:

[0007] Step 1: Equation construction:

[0008] Step 1.1: Construct the color-non-color combined spectral correction equation:

[0009] First, measure the true spectral reflectance R of several single-color fibers and mixed-color fibers at a wavelength of λ (in nm, the same below) act,λAnd converted into the ratio of the real absorption coefficient to the scattering coefficient of several monochromatic fibers and mixed-color fibers at wavelength λ (K / S) by formula (1) act,λ Then, according to formula (2) (formula (2) is the single constant Kubelka-Munk formula), the ratio of the calculated absorption coefficient to the scattering coefficient of the mixed color fiber (K / S) is obtained. mix,λ And compare its true value to fit the coefficient of formula (3) (formula (3) is the color-non-color combined spectrum correction formula), and then calculate the ratio of the predicted absorption coefficient to the scattering coefficient of the mixed color fiber at a wavelength of λ (K / S) according to formula (3) pre,λ Substitute it into formula (4) to obtain the predicted spectral reflectance R of the mixed color fiber at wavelength λ pre,λ ;

[0010]

[0011] In formula (1), R act,λ It is the true spectral reflectance of the fiber at wavelength λ, in %; (K / S) act,λ It is the ratio of the true absorption coefficient of the fiber to the scattering coefficient at wavelength λ;

[0012]

[0013] In formula (2), (K / S) i,λ is the K / S value of the i-th monochromatic fiber in the mixed color fiber at wavelength λ (i=1, 2, 3, 4, 5...n, the same below); (K / S) mix,λ is the K / S value of the mixed color fiber at a wavelength of λ calculated by formula (2); c i is the proportion of the i-th single-color fiber in the mixed-color fiber, c1+c2+...+c i =1;

[0014]

[0015] In formula (3), k1 and b1 are the spectral correction coefficients of colored fibers (such as red, yellow and blue); k2 and b2 are the spectral correction coefficients of non-colored fibers (such as white and black); (K / S) mix1,λ and (K / S) mix2,λ are the K / S values ​​of the mixed part of the colored fiber and the mixed part of the non-colored fiber in the mixed color fiber at a wavelength of λ; ε1 and ε2 are the adjustment factors of the colored fiber and the non-colored fiber, respectively. When (K / S) mix1,λ ≠0 (i.e. when the mixed color fiber contains colored fibers), ε1=1, otherwise ε1=0. When (K / S) mix2,λ≠0 (i.e. when the mixed color fiber contains non-color fiber), ε2=1, otherwise ε2=0; (K / S) pre,λ is the ratio of the predicted absorption coefficient to the scattering coefficient of the mixed-color fiber at wavelength λ;

[0016]

[0017] In formula (4), R pre,λ It is the predicted spectral reflectance of the mixed color fiber at wavelength λ, in %; (K / S) pre,λ is the ratio of the predicted absorption coefficient to the scattering coefficient of the mixed-color fiber at wavelength λ;

[0018] Step 1.2: Construct the optical structure color transfer equation of fiber-fabric:

[0019] First, measure the true spectral reflectance R of several monochromatic fibers at wavelength λ. fiber,λ And the true spectral reflectance R of several monochromatic fabrics at wavelength λ fabric,λ , and then calculate the color transfer parameters p1, p2 and p3 between the fiber and the fabric according to the reflectivity function relationship between the monochromatic fiber and the monochromatic fabric. Finally, the fiber-fabric optical structure color transfer equation is constructed as shown in formula (5);

[0020]

[0021] In the formula, R prefabric,λ is the predicted spectral reflectance of a single-color fabric at wavelength λ, in %; R fabric,λ is the true spectral reflectance of a single-color fabric at a wavelength of λ, in %; R fiber,λ is the true spectral reflectance of the monochromatic fiber at a wavelength of λ, in %; p1, p2 and p3 are all color transfer parameters between fiber and fabric;

[0022] Step 2: Prediction of reflectance from single-color fiber to mixed-color fabric:

[0023] First, according to formula (1) to formula (4), the predicted spectral reflectance R of the mixed color fiber at wavelength λ is obtained. pre,λ , and then according to formula (5) to obtain the predicted spectral reflectance R of the mixed color fabric at wavelength λ prefabric,λ (The predicted spectral reflectance R of the mixed color fiber at wavelength λ pre,λ Instead of the true spectral reflectance R of the monochromatic fiber at wavelength λ fiber,λ Substitute it into formula (5) for calculation, and the result is the predicted spectral reflectance R of the mixed color fabric at wavelength λ prefabric,λ ).

[0024] Preferably, in step 1, the sample masses of the single-color fibers and the mixed-color fibers are both 2-10 g, and when measuring the single-color fibers and the mixed-color fibers, the fibers must be arranged in parallel, have a fixed fiber stacking density, and be opaque.

[0025] Preferably, in step 1, the samples of the single-color fabric and the mixed-color fabric are folded 2 to 5 times, and the surfaces of the single-color fabric and the mixed-color fabric must be kept clean and opaque during measurement.

[0026] Preferably, in step 1, the wavelength range of λ is 380-700 nm and the wavelength interval is 10 nm.

[0027] Preferably, in step 2, the predicted reflectivity R of the mixed color fabric can be first prefabric,λ After conversion into Lab values, the color difference formula is used to evaluate the accuracy of the predicted color of mixed color fabrics.

[0028] Furthermore, the color difference formula is the CIELAB formula and the GB / T7921-2008 standard is implemented when calculating the color difference.

[0029] In the present invention, monochrome includes but is not limited to the five colors of red, yellow, blue, white and black; the number of training samples selected when constructing the equation includes but is not limited to five; the types of fibers include but are not limited to cotton fibers, wool fibers, cashmere fibers, kapok fibers, regenerated fibers and chemical fibers; monochrome yarns can be used as raw materials to replace monochrome fibers, and mixed-color yarns can be used as prediction materials to replace mixed-color fabrics; in addition, the present invention can be applied not only to the color prediction of colored fabrics, but also to training equations such as deep learning, neural networks and machine learning.

[0030] Beneficial effects of the present invention:

[0031] In past studies, the applicant has proposed a color-non-color combined spectral correction model based on a single constant KM model by studying the complex optical interactions of light in a fiber assembly (a combined non-linear spectral correction method for color and non-color fibers trained with mixed fiber training, publication number CN118446941A). This method can effectively improve the color prediction effect of the single constant KM model on mixed-color fibers, and achieve high-precision color prediction of monochrome fibers for mixed-color fibers.

[0032] Based on the color-non-color combined spectral correction model, the present invention takes into account the color difference caused by the optical structure difference between the fiber and the fabric, and thus proposes a fiber-fabric optical structure color transfer method suitable for rapid color spun fabric color prediction; the present invention can realize the color prediction of mixed color fabrics with monochromatic fibers, and simultaneously obtain accurate predicted reflectivity of mixed color fabrics, which not only solves the problem of color difference between the fiber and the fabric caused by optical structure difference, but also can effectively reduce the number and cost of fabric sample preparation (the method can realize color prediction from monochromatic fibers to mixed color fabrics on the basis of using only 5 monochromatic fibers, 5 monochromatic fabrics and 14 mixed color fiber samples as training samples), and the prediction accuracy is much higher than the existing single constant Kubelka-Munk (KM-1) model, double constant Kubelka-Munk (KM-2) model, Stearns-Noechel (SN) model and Friele model, and is expected to be applied to other materials with measurable spectra such as yarns and fabrics of different structures.

[0033] The features and advantages of the present invention will be described in detail through embodiments in conjunction with the accompanying drawings.

Brief Description of the Drawings

[0034] Figure 1 It is a physical picture of nine red, yellow and blue three-color mixed fiber samples and five white and black two-color mixed fiber samples;

[0035] Figure 2 It is a physical picture of five single-color fiber samples and five single-color fabric samples;

[0036] Figure 3 is a physical picture of 36 two-component mixed color fabric samples of Example 1;

[0037] FIG4(a), FIG4(b), FIG4(c) and FIG4(d) are respectively comparison diagrams of the predicted spectral reflectance and the actual spectral reflectance of the two-component mixed color fabric of Example 1;

[0038] Figure 5 is a comparison diagram of the predicted color and the actual color of the two-component mixed color fabric of Example 1;

[0039] Figure 6 is a comparison chart of the color difference results predicted by the method of the two-component mixed color fabric in Example 1 and the color difference results of other models;

[0040] Figure 7 is a physical picture of 36 three-component mixed color fabric samples of Example 2;

[0041] FIG8(a), FIG8(b), FIG8(c) and FIG8(d) are respectively comparison diagrams of the predicted spectral reflectance and the actual spectral reflectance of the three-component mixed color fabric of Example 2;

[0042] Fig. 9 is a comparison diagram of the predicted color and the actual color of the three-component mixed color fabric of Example 2;

[0043] Fig.10 is a comparison chart of the color difference results predicted by the method of the three-component mixed color fabric in Example 2 and the color difference results of other models;

[0044] Fig.11 is a physical picture of 15 four- and five-component mixed color fabric samples of Example 3;

[0045] FIG. 12( a ), FIG. 12( b ), FIG. 12( c ) and FIG. 12( d ) are comparison diagrams of the predicted spectral reflectance and the actual spectral reflectance of the four-component and five-component mixed color fabrics of Example 3, respectively;

[0046] Fig.13 is a comparison diagram of the predicted color and the actual color of the four- and five-component mixed color fabrics of Example 3;

[0047] Fig.14 This is a comparison chart of the color difference results predicted by this method for the four- and five-component mixed-color fabrics of Example 3 and the color results of other models. [Specific implementation method]

[0048] The present invention provides a fiber-fabric optical structure color transfer method suitable for rapid color textile fabric color prediction, which mainly involves the establishment of a reflectivity function relationship between the fiber and the fabric; the following uses five colored cotton fibers, red, yellow, blue, white, and black, to prepare single / mixed color fabric samples and mixed color fiber samples to verify the correctness of the present invention; it should also be noted that the present invention is not limited to the mixed prediction of these five colored cotton fibers, and is also applicable to target samples with less than / more than five required fibers or other materials. The specific steps of this method are as follows:

[0049] First, five colors of cotton fibers, red, yellow, blue, white, and black, were selected as monochrome fiber samples. Figure 1 As shown in the figure, nine red, yellow and blue three-color mixed fiber samples and five white and black two-color mixed fiber samples made by mixing the above five kinds of fibers are used as training samples (that is, nine kinds of color mixed fiber samples and five kinds of non-color mixed fiber samples in different proportions are prepared by five kinds of monochromatic fibers as training samples); then, according to step 1.1, the real spectral reflectance R of each monochromatic fiber and the mixed fiber training sample is measured by using a Datacolor 600 spectrophotometer. act,λ Substitute it into formula (1) to get the true (K / S) act,λ, then combine formula (2) with formula (3) and fit them by the least square method to obtain the color and non-color fiber spectral correction coefficients k1, b1 and k2, b2 respectively, which are applied to formula (3), and finally the color-non-color combined spectral correction equation is constructed (this equation can be used for color prediction of mixed color fiber samples).

[0050] Secondly, if Figure 2 As shown in FIG. 1 , the five monochromatic fibers are firstly prepared into five monochromatic plain knitted fabrics (the horizontal and vertical densities of the fabrics are both 14 / cm), and then the five monochromatic fiber samples and the five monochromatic fabric samples are used as training samples; then, the real spectral reflectance R of each monochromatic fiber and fabric training sample is measured respectively according to step 1.2 using a Datacolor 600 spectrophotometer. fiber,λ With R fabric,λ , and then substitute into formula (5) to construct the fiber-fabric optical structure color transfer equation (this equation can be used to predict the color of fabric samples of the same specifications made of the same type of fiber of any color).

[0051] Finally, the predicted spectral reflectance R of the mixed-color fiber is obtained by using the color-non-color combined spectral correction equation from the monochromatic fiber. pre,λ , and then the predicted spectral reflectance R of the mixed color fabric is calculated by the fiber-fabric optical structure color transfer equation prefabric,λ , and finally realize the color prediction of mixed-color fabrics through single-color fibers.

[0052] Example 1: Investigation of color prediction accuracy of two-component mixed color fabrics:

[0053] See also Figure 3 Using red, yellow and blue colored fibers and white and black non-colored fibers as raw materials, the above five single-color fibers were mixed in pairs at a gradient ratio of 10% to make 36 two-color mixed-color fabric samples (the horizontal and vertical densities of the fabrics were both 14 / cm); these fabrics were used to verify the accuracy of the present invention in color prediction of two-component mixed-color fabrics.

[0054] First, use Datacolor 600 spectrophotometer to measure the true spectral reflectance R of each mixed color fabric. fabric,λ And as the true value of each mixed color fabric, then compare the above blending ratio with the (K / S) of the single color fiber act,λ Substitute into formula (2) and formula (3) to calculate the corresponding mixed color fiber (K / S) pre,λ , and then substitute it into formula (4) to obtain the predicted spectral reflectance R of the mixed color fiber pre,λ Finally, the predicted spectral reflectance of the mixed color fiber is converted into the predicted value R of the mixed color fabric through formula (5) prefabric,λ .

[0055] The comparison between the predicted and actual spectral reflectance of some verification samples is shown in Figures 4(a), 4(b), 4(c) and 4(d); it can be seen that the reflectance of the mixed color fabric verification sample predicted by the present invention is very consistent with the actual reflectance.

[0056] Convert the predicted spectral reflectance and actual spectral reflectance of the verification sample into Lab values ​​and perform color comparison; refer to Figure 5 (The one with the sample number mark is the actual color of the verification sample, and the one without the mark is the predicted color). The color difference between the two is difficult to be identified by the human eye.

[0057] In addition, the color difference of the present invention and the other four original color prediction models are calculated and compared using the CIELAB color difference formula (the results are shown in Table 1 below), and then all the color difference results of the present invention and the other four models are compared using a box plot (the results are shown in Table 1 below). Figure 6 shown).

[0058] Table 1 Comparison of predicted color difference of two-component mixed color fabric samples

[0059]

[0060] The results show that the average color difference and the maximum color difference of the two-component mixed-color fabric samples obtained by the present invention are 0.68 and 1.47 respectively (the results are lower than those of the other four models); this proves that the fiber-fabric optical structure color transfer method proposed in the present invention can effectively realize the color prediction of monochrome fiber to mixed-color fabric, and the accuracy and stability of the prediction results are better than other existing models.

[0061] Example 2: Investigation of the accuracy of color prediction on three-component mixed-color fabrics:

[0062] See also Figure 7 , using red, yellow and blue colored fibers as raw materials, the above three monochromatic fibers were made into 36 three-color mixed-color fabric samples (the horizontal and vertical densities of the fabrics were both 14 / cm) with a proportional gradient of 10 to 80% in a 10% change step; these fabrics were used to verify the accuracy of the present invention in the color prediction of three-component mixed-color fabrics.

[0063] Similarly, prediction is performed using the same method as in the first embodiment.

[0064] Figures 8(a), 8(b), 8(c) and 8(d) show the comparison results of the predicted and actual reflectances of some verification samples, which proves that the reflectance of the mixed color fabric verification sample predicted by the present invention is well consistent with the actual reflectance.

[0065] Convert the predicted spectral reflectance and actual spectral reflectance of the verification sample into Lab values ​​and perform color comparison; refer to Fig. 9(The one with the sample number mark is the actual color of the verification sample, and the one without the mark is the predicted color). The color difference between the two is difficult to be identified by the human eye.

[0066] In addition, the CIELAB color difference formula is first used to calculate and compare the color difference of the present invention and the other four original color prediction models (the results are shown in Table 2 below), and then a box plot is used to compare all the color difference results of the present invention and the other four models (the results are shown in Table 2 below). Fig.10 shown).

[0067] Table 2 Comparison of predicted color difference of three-component mixed color fabric samples

[0068]

[0069]

[0070] The results show that the average color difference and the maximum color difference of the three-component mixed-color fabric samples obtained by the present invention are 0.81 and 1.49 respectively (the average color difference is only slightly higher than the KM-2 model, but the maximum color difference is significantly better than the KM-2 model, and the method proposed by the present invention is still significantly better than the four models); this proves that the fiber-fabric optical structure color transfer method proposed by the present invention can effectively realize the color prediction of monochrome fiber to mixed-color fabric, and the accuracy and stability of the prediction results are better than other existing models.

[0071] Example 3: Study on the color prediction accuracy of four- and five-component mixed-color fabrics:

[0072] See also Fig.11 , using red, yellow and blue colored fibers and white and black non-colored fibers as raw materials, the above five monochromatic fibers were mixed in random proportions to make 15 four- and five-component mixed-color fabric samples (the horizontal and vertical densities of the fabrics were both 14 / cm); these fabrics were used to verify the accuracy of the present invention in color prediction of four- and five-component mixed-color fabrics.

[0073] Similarly, prediction is performed using the same method as in the first embodiment.

[0074] Figures 12(a), 12(b), 12(c) and 12(d) show the comparison results of the predicted and actual reflectances of some verification samples, which proves that the reflectance of the mixed color fabric verification sample predicted by the present invention is well consistent with the actual reflectance.

[0075] Table 3 Comparison of predicted color difference of four- and five-component mixed color fabric samples

[0076]

[0077] The results show that the average color difference and maximum color difference of the four- and five-component mixed-color fabric samples obtained by the present invention are 0.95 and 1.82 respectively (the average color difference is only slightly higher than the SN model, but the maximum color difference is better than the SN model, and the method proposed by the present invention is still significantly better than the four models); this proves that the fiber-fabric optical structure color transfer method proposed by the present invention can effectively realize the color prediction of monochrome fiber to mixed-color fabric, and the accuracy and stability of the prediction results are better than other existing models.

[0078] The above embodiments are intended to illustrate the present invention, not to limit the present invention. Any solution that is a simple transformation of the present invention belongs to the protection scope of the present invention.

Claims

1. A method for rapid color prediction of a yarn-dyed fabric, characterized in that: The steps include: Step 1: Equation construction: Step 1.1: Construct the color-non-color combined spectral correction equation: First, measure the true spectral reflectance R of several monochromatic fibers and mixed-color fibers at wavelength λ. act,λ And converted into the ratio of the real absorption coefficient to the scattering coefficient of several monochromatic fibers and mixed-color fibers at a wavelength of λ (K / S) by formula (1) act,λ , and then according to formula (2) to obtain the ratio of the calculated absorption coefficient to the scattering coefficient of the mixed color fiber (K / S) mix,λ And compare its true value to fit the coefficient of formula (3), and then calculate the ratio of the predicted absorption coefficient to the scattering coefficient of the mixed color fiber at a wavelength of λ (K / S) according to formula (3): pre,λ Substitute it into formula (4) to obtain the predicted spectral reflectance R of the mixed color fiber at wavelength λ pre,λ ; In formula (1), R act,λ is the true spectral reflectance of the fiber at wavelength λ, in %; (K / S) act,λ It is the ratio of the true absorption coefficient of the fiber to the scattering coefficient at wavelength λ; In formula (2), (K / S) i,λ is the K / S value of the i-th monochromatic fiber in the mixed color fiber at wavelength λ; (K / S) mix,λ is the K / S value of the mixed color fiber at a wavelength of λ calculated by formula (2); c i is the proportion of the i-th single-color fiber in the mixed-color fiber, c1+c2+...+c i =1; In formula (3), k1 and b1 are the spectral correction coefficients of colored fibers; k2 and b2 are the spectral correction coefficients of non-colored fibers; (K / S) mix1,λ and (K / S) mix2,λ are the K / S values ​​of the mixed part of the colored fiber and the mixed part of the non-colored fiber in the mixed color fiber at a wavelength of λ; ε1 and ε2 are the adjustment factors of the colored fiber and the non-colored fiber, respectively. When (K / S) mix1,λ ≠0, ε1=1, otherwise ε1=0, when (K / S) mix2,λ ≠0, ε2=1, otherwise ε2=0; (K / S) pre,λ is the ratio of the predicted absorption coefficient to the scattering coefficient of the mixed-color fiber at wavelength λ; In formula (4), R pre,λ It is the predicted spectral reflectance of the mixed color fiber at wavelength λ, in %; (K / S) pre,λ is the ratio of the predicted absorption coefficient to the scattering coefficient of the mixed-color fiber at wavelength λ; Step 1.2: Construct the optical structure color transfer equation of fiber-fabric: First, measure the true spectral reflectance R of several monochromatic fibers at wavelength λ. fiber,λ And the true spectral reflectance R of several monochromatic fabrics at wavelength λ fabric,λ , and then calculate the color transfer parameters p1, p2 and p3 between the fiber and the fabric according to the reflectivity function relationship between the monochromatic fiber and the monochromatic fabric. Finally, the fiber-fabric optical structure color transfer equation is constructed as shown in formula (5); In the formula, R prefabric,λ is the predicted spectral reflectance of a single-color fabric at wavelength λ, in %; R fabric,λ is the true spectral reflectance of a single-color fabric at a wavelength of λ, in %; R fiber,λ is the true spectral reflectance of the monochromatic fiber at a wavelength of λ, in %; p1, p2 and p3 are all color transfer parameters between fiber and fabric; Step 2: Prediction of reflectance from single-color fiber to mixed-color fabric: First, according to formula (1) to formula (4), the predicted spectral reflectance R of the mixed color fiber at wavelength λ is obtained. pre,λ , and then according to formula (5) to obtain the predicted spectral reflectance R of the mixed color fabric at wavelength λ prefabric,λ .

2. The rapid color prediction method for a yarn-dyed fabric as claimed in claim 1, characterized in that: In step 1, the sample weight of the single-color fiber and the mixed-color fiber is 2-10 g. When measuring the single-color fiber and the mixed-color fiber, it is necessary to ensure that the fibers are arranged in parallel, the fiber stacking density is fixed, and the fibers are opaque.

3. The rapid color prediction method for a yarn-dyed fabric as claimed in claim 1, characterized in that: In step 1, the samples of the single-color fabric and the mixed-color fabric are folded 2 to 5 times. When measuring the single-color fabric and the mixed-color fabric, the surface of the fabric must be clean and opaque.

4. The rapid color prediction method for a yarn-dyed fabric as claimed in claim 1, characterized in that: In step 1, the wavelength range of λ is 380-700 nm and the wavelength interval is 10 nm.

5. The rapid color prediction method for a yarn-dyed fabric as claimed in claim 1, characterized in that: In step 2, the predicted reflectivity R of the mixed color fabric can be prefabric,λ After conversion into Lab values, the color difference formula is used to evaluate the accuracy of the predicted color of mixed-color fabrics.

6. The rapid color prediction method for a yarn-dyed fabric as claimed in claim 5, characterized in that: The color difference formula is the CIELAB formula and the GB / T 7921-2008 standard is implemented when calculating the color difference.

Images