The four-roller compact spinning system is widely used today and is considered an improvement over traditional ring spinning because of its excellent yarn properties. Reducing energy consumption in compact spinning has always been a key issue for the textile industry. This paper proposed a method for installing an air guide on compact spinning to reduce energy consumption. Four types of air guides were designed with different side shapes (Type-N, Type-L, Type-R, and Type-LR), and the airflow characteristics in the converging zone were analyzed using numerical simulation. In addition, high-speed photography technology and image-processing technology were used to study the dynamic state of the fiber bundle in the flow field of the converging zone. The effectiveness of the preferred solution of the air guide was also verified by spinning experiments. The results revealed that the best air guide was Type-LR, achieving a 3.7% reduction in yarn hairiness even after a decrease in negative pressure by 800 Pa and a 58.4% reduction in power of the fan motor, surpassing the yarn quality observed without any air guide.
Objective In order to solve the problems of slow speed,low efficiency,and high cost in conventional manual quality inspection methods for sewing thread,this study proposes a machine vision-based method for sewing thread defect detection in seams.This study aims to achieve fast,accurate,and automated identification of common defects such as cast thread,jumper thread,and broken thread in seams.This study also highlights the importance and necessity of improving product quality and production efficiency in the textile and garment industry. Method This study adopts a two-step approach for defect detection.Firstly,a low-cost array camera was adopted to capture real-time images of the sewing seam and the DeblurGAN-v2 method was employed to remove motion blurriness from the images,aiming at improving image clarity.Secondly,the student-teacher feature pyramid matching method was applied for anomaly detection,which transfers the knowledge from a pre-trained ResNet-34 model as the teacher network to a student network with the same architecture,so as to learn the distribution of normal images.By comparing the differences between the feature pyramids generated by the two networks as a scoring method,the defect detection system made decisions on whether the image has anomalies,and marked the abnormal areas with a heat distribution map. Results The defects of flat stitch fabric and overstitch fabric were tested and the performance of the proposed method was evaluated in terms of recall and accuracy rates.The results show that the proposed method can effectively detect various sewing thread defects and has high recall and accuracy rates for different types of defects.This study also provided some examples of defect detection results and scores for different types of defects. Conclusion The feature pyramid matching technique is applied in the field of stitch trace detection.By adding the difficult sample mining technology,the average detection accuracy is increased to more than 95%,and the detection speed of a single image is less than 0.04 s.Aiming at image motion blur ring caused by jitter and fast movement.The DeblurGAN-v2 framework is used as the framework of deblurring algorithm,and the blueprint convolution is added to change the backbone network,and the processing speed of a single image is kept below 0.06 s.The model has excellent interference resistance and high processing speed,and can meet the requirement of stitch trace recognition.
Objective The detection of bobbin yarn margin is an important part of the automatic production process in the textile industry,of which the design of an image algorithm is a key part for facilitating the automatic bobbin change system.Because the bobbin installed on the take-up bar rotates in two directions and the front image of the bobbin is oval,it is necessary to design a bobbin yarn margin detection algorithm suitable for the actual production situation. Method In view of the special imaging effect of the bobbin and considering the actual production situation,the inverse projection transformation algorithm was designed based on the multi-point perspective model.The initial inverse projection transformation matrix was calculated according to the spatial position relationship between the bobbin and the camera,and then the bobbin area was segmented to obtain the inner and outer bobbin areas of the bobbin and calculate the position information of the inner and outer bobbin areas.The compensation matrix was set,and the transformation matrix was optimized according to the position information of the inner and outer bobbin and the element transformation characteristics of the transformation matrix.The polar coordinates of the image were expanded after the inverse projection transformation and the bobbin yarn margin was calculated according to the set yarn margin calculation criteria. Results The processing effect of this method and the experimental results were discussed in detail.The inverse projection transformation effect of the algorithm was compared with that of the conventional three-point perspective model.It was seen that the conventional inverse projection transformation was difficult to consider the transformation effect of the inner and outer cylinders,and the yarn width in the same direction of the yarn area was not equal or even different.Therefore,the compensation matrix was set based on conventional inverse projection transformation to optimize the effect of inverse projection transformation.The element value of the compensation matrix was determined according to the position relationship between the inner and outer barrels of the bobbin.Therefore,the inner and outer barrels of the bobbin were first segmented,and the contour of the inner and outer barrels was fitted.The position information of the inner and outer cylinder,such as roundness,direction angle and area size,were calculated by obtaining the contour of the inner and outer cylinder.The compensation matrix was solved according to the element transformation characteristics of the compensation matrix after obtaining the position information of the inner and outer drums and the yarn area width at the inner and outer drums.Taking the square calibration board as an example,the transformation characteristics of the compensation matrix elements were shown in compensation matrix factor transformation effect.The fifth bobbin was selected to calculate the compensation matrix,and the product of the initial transformation matrix and the compensation matrix were as the inverse projection transformation matrix.Considering the limitations of the transformation effect,the conventional ellipse polar coordinates were optimized,and the angle information of the ellipse was added to the polar coordinate transformation to obtain the polar coordinate expansion diagram of the corrected bobbin image.The yarn margin of the bobbin was obtained according to the specified yarn margin calculation criteria.The objective in this paper was to determine different types of yarn bobbins under the sampling angle,so that the two types of yarn bobbins could be considered separately in the experiment.The test results of partial yarn margin of cylindrical bobbin were shown in margin detection results.It was seen that the test error was mostly within 5 mm,but it reached over 10%under some circumstances.In fact,although the error of the bobbin with large margin is relatively large,it will not have a significant impact on the actual production.This is because the main purpose of this algorithm is to provide warnings when there is less yarn surplus.Therefore,as long as the yarn surplus is detected before it is used up,the system can calculate the remaining usage time based on the yarn usage speed.The test results of part of the yarn margin of the cone bobbin were shown in margin detection result.The test error was within 4 mm,the error accuracy was about 95%,and the yarn width of the sample with large error was within 40 mm. Conclusion In order to solve the problems of poor transformation effect and high requirement of experimental conditions in general inverse projection transformation algorithm,this paper proposes a yarn margin detection algorithm based on inverse projection transformation based on multi-point perspective model.The experimental results show that the algorithm proposed in this paper can obtain good transformation effect of inner and outer bobbin at the same time for different types of bobbins under the determined sampling angle,and the detection error of yarn margin is within 5 mm,which meets the practical production requirements.Compared with other inverse projection transformation algorithms,this algorithm is simpler to implement and has higher detection efficiency.In the future work,the bobbin at different angles and different distances,and design filters to further reduce the interference of external environmental factors on the algorithm will be further studied,to enhance the detection accuracy of the algorithm.
针对液体分散染料明显区别于粉状分散染料的应用性能和生态环保性,简述了液体分散染料发展历程和染料研磨技术要点,从染料应用性能方面评述了液体分散染料的质量控制因素及分析了已建立的液体分散染料稳定性快速评价指标的合理性,阐述了液体分散染料替代粉状分散染料的应用进展及连续热熔染色的新用途和需要解决的新问题.指出:液体分散染料应建立区别于粉状分散染料的稳定性和应用性能相协调的评价指标及质量标准;染色中选择较小的轧槽容积和均匀轧车以及增加轧前的喷淋装置都有利于染料对织物的渗透和织物着色均匀性;同时,研究集染色和功能性于一浴的功能液体分散染料,将是一种可从源头满足印染行业的重大节能减排技术需求.
为开发功能性含锌和含铜纤维及医用敷料在伤口护理中的应用,介绍了锌离子和铜离子的生物活性及其在伤口愈合过程中的作用,总结了国内外市场上各种含锌和含铜医用敷料及其制备方法,重点介绍了含锌和含铜的海藻酸盐纤维、壳聚糖纤维,以及通过静电纺丝、高分子材料复合加工、纳米技术等先进工艺制备的各种含锌和含铜医用敷料及其释放锌离子和铜离子的性能.指出锌离子和铜离子具有优良的抗菌及促进伤口愈合的功效,含锌和含铜医用敷料通过释放锌离子和铜离子,对创面常见的各种细菌有很强的抑菌作用,在烧伤、下肢溃疡、糖尿病足溃疡、褥疮等伤口护理中有重要的应用价值.
