聚对苯二甲酸丙二醇酯与聚对苯二甲酸丁二醇酯混纺纤维的智能识别

针对熔融显微投影法无法解决对聚对苯二甲酸丙二醇酯（PTT）纤维和聚对苯二甲酸丁二醇酯（PET）纤维的智能识别和根数的自动统计技术问题，采用图像处理方法对PTT 纤维与PBT 纤维混纺纱的显微图像进行前处理，获得较理想的二值化图像；再对二值化图像中各交叉纤维进行智能分离，解决纤维根数自动统计问题；然后提取每根纤维等效直径、色度和纤维伸直度3个特征值；再以200 根纤维的3个特征值矩阵为学习样本，建立BP 神经网络，以20根纤维作为检验样本。结果显示：该网络对PTT 纤维及PBT 纤维的识别率高于99%，成功地将混合纤维智能识别；采用该法对1 000根混纺纤维进行处理，并采用熔融显微投影法进行测试，PTT纤维及PBT 纤维混纺比为38%及62%或32%及68%，与对应实际混纺比4：6 或3：7相比，误差在± 3%之内。

Intelligent determination of blending fiberfor polytrimethylene terephthalate and polybutylene terephthalate

The most effective method to determinepolytrimehylene terephthalate (PTT)fiber and polybutylene terephthalate (PBT) fiber blending ratio, themelted micro projection method is difficult to achieve intelligent identification and automatically count number of fibers.To solve this technical problem, the microscopic image of PBT fiber and PTT fiber blended was preprocessed by image processing technology to obtain the ideal thresholding image, and PBT and PTT fibers was intelligently separated and automatically counted; then the three eigenvalues of fitness, chromaticity and fiber straightness of each fiber was extracted, and three eigenvalues matrix of 200 fibers were used as learning samples to establish a BP neural network, and twenty fiber were used to verify that. The results show that the recognition rate of PTT fiber and PBT fiber are higher than 99%. The mixed fibers are intelligently identified by this method successfully. The images of 1 000 blended fibers are processed in the same way. The blending ratio of PTT fiber and PBT fiber is calculated by the melted micro projection method to be 38% and 62% or 32% and 68%. Compared with the corresponding actual blending ratio (40% and 60% or 30% and 70%), the error was within ±3%.