Design and Numerical Simulation of Low-Fiber Hollow Spindle in Air-Jet Vortex Spinning

Based on the mechanical system of free-end fibers and the analysis of pulling free-end fibers out of the spun yarn during spinning,a low-fiber hollow spindle is designed and the air distribution of fluent field is simulated numerically. The negative pressure effect is much bigger at the top of low-fiber hollow spindle than that in Murata No.861,which is more conducive for single fiber to get into the channel of hollow spindle. The tangential velocity in 0-3 mm at the top of hollow spindle increases and the fluctuation of radial velocity is much stronger,which enhance the wrapping effect. In the addition,the distribution of axial velocity remains the same.     

Characterizing and Analyzing the Airflow Field inside the Nozzle Block of Murata Vortex Spinning

A three-dimensional computational fluid dynamics model is developed by software Fluent 6.2, to simulate the flow field inside the nozzle block of the Murata vortex spinning. The flowing state and the distribution law of static pressure and velocity are characterized and analyzed. The relationship between the flowing state and the structure of the vortex spun yarn is also discussed. The research results can enhance the understanding of the yarn formation principle from viewpoint of the airflow field law inside the nozzle block of Murata vortex spinning.     

集聚纺的纱线强力理论

为了从理论上分析集聚纺纱线较环锭纺纱线强力提高的原因,基于两种纱线的结构差异,如纤维的转移、纤维的堆积密度、纱线的直径等,建立了一个纱线结构计算模型.从理论上分析了纤维伸长率分布、纤维堆积密度及纱线直径对纱线强力的影响,合理解释了集聚纺纱线强力上升的原因.     

滑动轴承上的曲柄摇杆机构

通过振动谱线图,发现连杆角速度等于零的位置是滑动轴承含间隙的曲柄摇杆机构强振动的高发区域.用含间隙滑动轴承的轴颈中心轨迹,研究和分析了含运动副间隙的四连杆机构的振动特点,最强振动发生在连杆角速度为零和轴颈中心轨迹偏心率小于0.1时.并通过连杆、滑动轴承系统的运动方程,研究轴颈静平衡位置稳定性和油膜支承轴颈振动的特点.     

Dynamic Analysis of Four-Bar Beating-up Mechanism with Joint Clearance

For the four-bar beating-up mechanism of air-jet loom,the plain bearing of linkage is the bearing with dynamic load,and is immersed in the lubricant-box.If the joint clearance is considered,the research on linkage movement could be very complicated.In this paper,the kinematic characteristics of four-bar beating-up mechanism with joint clearance were studied by analyzing the trace of journal center and the balance of radial,tangential forces,and bearing load.The region of principal vibration and its forming ...     

Application of Tencel on Raschel Blanket

For excellent properties,Tencel is more and more popular in textile industry.Four kinds of raschel knitted blanket test samples were woven with the base-weave polyester yarns and velvet yarns,respectiv...     

Comparison of Flow Field Distribution and Characteristic on Different Compact Spinning Systems

Numerical simulation models of compact spinning with perforated drum,punched apron and lattice apron were established and computerized with computational fluid dynamics (CFD)software.Characteristics of airflow in condensing zone were achieved.Velocity vectors of the flow on different systems were contrasted and discussed.Then,trajectories of fibers in condensing zone were simulated by a specially designed MATLAB program routine.The result of the simulation clarified the compact mechanism of the compact spinning systems.Also,it applied a guideline for reasonable utilization of the airflow in condensing zone.     

Quantitative Risk Assessment with Data Mining for Formaldehyde Content of Imported Textile

The assessment of potential risks posed by formaldehyde in clothing to consumers is of increasing concern worldwide,for free formaldehyde is harmful to human. Some papers focus on hazard and exposure assessment,using quantitative and semi-quantitative method, rather than assessment for the factors related to formaldehyde content. In order to perform early-warning research and risk management,the quantitative risk assessment is used to analyze the imported textile testing data and find the factors related to formaldehyde content. K-means algorithm is the core of the method. It can be concluded that the disqualified textiles have relations with main fiber components,fabric color,season month,and brand. Then the data in the same attribute are clustered by Kmeans algorithm. The different classes are defined as related danger level,respectively severe,moderate,and light.