Predicting the Hairiness of Cotton Rotor Spinning Yarns by Artificial Intelligence

Pretreatment dependence of mechanical and surface properties of cotton ring- and OE rotor-spun yarns

The variations in the mechanical and surface properties of cotton ringand OE rotor-spun yarns on exposure to different chemicals during wet processing have been studied. It is observed that the structure and yarn twist are the dominant parameters in determining the accessibility of chemicals during wet processing. The most distinct outcome of alkaline scouring of cotton yarns, in comparison to ...

full text

Using Artificial Neural Network Algorithm to Predict Tensile Properties of Cotton-Covered Nylon Core Yarns

Artificial Neural Networks are information processing systems. Over the past several years, these algorithms have received much attention for their applications in pattern completing, pattern matching and classification and also for their use as a tool in various areas of problem solving. In this work, an Artificial Neural Network model is presented for predicting the tensile properties of co...

full text

The Effect of Yarn Hairiness on Air Drag in Ring Spinning

Air drag on yarn and package surfaces affects yarn tension, which in turn affects energy consumption and ends-down in ring spinning. This paper investigates the effects of yarn hairiness on air drag in ring spinning. Theoretical models of skin friction coefficient on the surface of rotating yarn packages were developed. The predicted results were verified with experimental data obtained from co...

full text

Using Artificial Neural Network Algorithm to Predict Tensile Properties of Cotton-Covered Nylon Core Yarns

Artificial Neural Networks are information processing systems. Over the past several years, these algorithms have received much attention for their applications in pattern completing, pattern matching and classification and also for their use as a tool in various areas of problem solving. In this work, an Artificial Neural Network model is presented for predicting the tensile &#10properties of ...

full text

Future prospects of rotor spinning

Labour productivity has been continuously increasing since the days of spinningjenny to the modern day roto r frames. To visualize the spinning industry in the year 2000, the past and the present technologica l changes have been analyzed so as to assess their overall impact in terms of labour complement.. material handling, required operative skill and automation. The automation in textile indu...

full text

Optimisation of Cotton Fibre Blends using AI Machine Learning Techniques

Fibre blend should be composed regarding the requirements and allowable price of the textile end product. Using the appropriate raw material and optimised fibre blends we can influence the mechanical properties and regularity of a yarn as well as significantly reduce the number of yarn faults. The contribution presents a study of the influence of quality characteristics of cotton fibres and con...

full text

Predicting the Hairiness of Cotton Rotor Spinning Yarns by Artificial Intelligence

Authors: not saved

Abstract:

Upgrade to premium to download articles

Pretreatment dependence of mechanical and surface properties of cotton ring- and OE rotor-spun yarns

Using Artificial Neural Network Algorithm to Predict Tensile Properties of Cotton-Covered Nylon Core Yarns

The Effect of Yarn Hairiness on Air Drag in Ring Spinning

Using Artificial Neural Network Algorithm to Predict Tensile Properties of Cotton-Covered Nylon Core Yarns

Future prospects of rotor spinning

Optimisation of Cotton Fibre Blends using AI Machine Learning Techniques