Fiber identification method and measurement method for content of fiber constituent in blended fabric
A technology for fiber identification and blended fabrics, which is applied in measuring devices, analysis materials, instruments, etc., can solve the problems of only random sampling, damage to the fiber products to be tested, and inability to meet efficient and fast detection, and achieve accurate and reliable measurement results
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Embodiment 1
[0051] Samples: Two unknown fiber samples from a certain manufacturer. In order to exclude the interference of color, the samples were all white.
[0052] Identification steps:
[0053] (1) Measure the rotational linear polarization of two unknown fiber samples respectively, and obtain the LDP(θ i , θ s ) formula, and parameters A, B, C, etc. are obtained at the same time.
[0054] (2) Extract the values of A and B to obtain a new parameter α=A / B, and perform histogram statistics on the parameter α to obtain characteristic curves of two unknown fibers to be compared.
[0055] (3) Compare the measured characteristic curves of the two unknown fibers with various standard characteristic curves in the standard library one by one, and obtain the correlation coefficient r between the curves respectively, and the results are shown in Table 1.
[0056] Table 1 Correlation coefficient between tested sample and standard sample
[0057] Correlation coefficient r
co...
Embodiment 2
[0063] Sample: A white blended sample, two types of blended fibers are known.
[0064] Identification steps:
[0065] (1) Perform rotational linear polarization measurement on unknown blended fiber samples, and fit to obtain LDP(θ i , θ s ) formula, and parameters A, B, C, etc. are obtained at the same time.
[0066] (2) Extract the value combination of A and B to obtain a new parameter α=A / B, and make histogram statistics on the parameter α to obtain the α characteristic curve of the blended fiber.
[0067] (3) Select the α characteristic curves of the two fibers in the standard database as the fiber components for simulating blending.
[0068] (4) Take a set of weight values for linear superposition to obtain a blended simulation α characteristic curve. Wherein, the weight value is obtained by taking an initial weight value set (such as 0.01 / 0.99), and changing it with a certain step size (such as 0.01) until the upper limit of the ratio (such as 0.99 / 0.01).
[0069] ...
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