Ring spun yarn quality prediction using hybrid neural networks

2021 ◽  
pp. 1-9
Author(s):  
Hanen Ghanmi ◽  
Adel Ghith ◽  
Tarek Benameur
Keyword(s):  
Neural Networks ◽  
Quality Prediction ◽  
Yarn Quality ◽  
Spun Yarn ◽  
Ring Spun Yarn ◽  
Hybrid Neural Networks

scholarly journals Impact of Carding Parameters and Draw Frame Doubling on the Properties of Ring Spun Yarn

2013 ◽  
Vol 8 (2) ◽  
pp. 155892501300800 ◽  
Author(s):  
Abdul Jabbar ◽  
Tanveer Hussain ◽  
Abdul Moqeet
Keyword(s):  
Experimental Design ◽  
Production Rate ◽  
Quality Parameters ◽  
Cotton Yarn ◽  
Yarn Quality ◽  
Spun Yarn ◽  
Ring Spun Yarn ◽  
Yarn Tenacity ◽  
The Impact

The impact of card cylinder speed, card production rate and draw frame doubling on cotton yarn quality parameters was investigated by using the Box-Behnken experimental design. It was found that yarn tenacity, elongation and hairiness increase by increasing the number of draw frame doubling up to a certain level and then decrease by further increase in doubling. Yarn unevenness increased by increasing card production rate and total yarn imperfections increased by decreasing card cylinder speed and increasing card production rate.

Effect of can-storage parameters of finisher drawframe on combed ring spun yarn quality

2019 ◽  
Vol 23 (2) ◽  
pp. 153-167
Author(s):  
Sukhvir Singh ◽  
Niranjan Bhowmick ◽  
Anand Vaz
Keyword(s):  
Storage Time ◽  
Statistical Significance ◽  
Research Work ◽  
Quality Parameters ◽  
Vital Role ◽  
Spring Stiffness ◽  
Content Type ◽  
Yarn Quality ◽  
Spun Yarn ◽  
Ring Spun Yarn

Purpose The present work is a systematic study to understand the cause of poor quality of sliver, roving and yarn due to defective sliver storage can-spring at finisher drawframe machine in spinning preparatory. This study aims to investigate the influence of can-spring stiffness factor, sliver deposition rate and sliver coils position on yarn unevenness and thin places considering two cases of sliver storage time. Design/methodology/approach Combed ring spun yarn samples were produced by varying finisher drawframe variables, which were can-spring stiffness, delivery speed and sliver coils position in storage can. For research design, three-factor three levels of Box-Behnken experimental design was adopted. To investigate the effect of sliver storage time on combed yarn unevenness and thin places, yarn samples were produced at 8 h sliver storage time and without allowing any storage time. Sliver storage time is the time for which combed drawn sliver kept idle in storage cans before feeding to speedframe machine. The 8 h sliver storage time was considered for present study after consulting industrial experts. Adequate numbers of the samples were tested for yarn quality parameters such as yarn unevenness and thin places 50 per cent/km on standard instruments. Finally, the test results were analyzed using statistical software to check the statistical significance of all the independent variables on observed response through analysis of variance. Findings The experimental results showed that the yarn samples produced from older can-springs and bottom position sliver coils stored at 8 h storage time were showing higher yarn unevenness and thin places compared to other yarn samples. The results also showed that the effect of delivery speed is not significant on yarn unevenness for samples produced without allowing any sliver storage time. Research limitations/implications The present study is an outcome of a practical problem experienced at the finisher drawframe machine in a spinning industry. For this purpose, only scrutinized finisher drawframe variables were considered for the evaluation. There are many equally important other factors, which were not considered due to research work feasibility. Social implications This paper investigates the effect of some imperative factors at the finisher drawframe stage on combed yarn quality. The present study will boost existing knowledge of the spinner’s community regarding the effect of can-spring stiffness, sliver coils position and storage time on resultant combed yarn quality parameters. Originality/value The work is original and only a few references are available. The study reveals that storage can-spring stiffness should be chosen carefully for better sliver handling. It is observed that finisher drawframe can-spring stiffness, sliver storage time and sliver coils position play a vital role in deciding quality characteristics of stored sliver and ultimately affect yarn quality.

Taguchi approach for analyzing the impact of varying degrees of cotton stickiness on ring-spun yarn quality

2016 ◽  
Vol 108 (3) ◽  
pp. 408-411 ◽  
Author(s):  
Hafiz A. Eltahir ◽  
H. Abderahman ◽  
Salah Abdelateef ◽  
Salah Aldeen M. Elarabi
Keyword(s):  
Taguchi Approach ◽  
Yarn Quality ◽  
Spun Yarn ◽  
Ring Spun Yarn ◽  
The Impact

Relationships of cotton fiber properties to ring-spun yarn quality on selected High Plains cottons

2012 ◽  
Vol 82 (4) ◽  
pp. 400-414 ◽  
Author(s):  
William Brock Faulkner ◽  
Eric F Hequet ◽  
John Wanjura ◽  
Randal Boman
Keyword(s):  
Cotton Fiber ◽  
High Plains ◽  
Fiber Properties ◽  
Yarn Quality ◽  
Spun Yarn ◽  
Ring Spun Yarn

Optimization of Break Draft, Pin Spacer and Rubber Cots Hardness to Enhance the Quality of Ring Spun Yarn Using Factorial Design

2018 ◽  
Vol 13 (2) ◽  
pp. 155892501801300 ◽  
Author(s):  
Furqan Khurshid ◽  
Sarmad Aslam ◽  
Usman Ali ◽  
Amir Abbas ◽  
Talha Ali Hamdani ◽  
...  
Keyword(s):  
Quality Parameters ◽  
Ring Spinning ◽  
Technological Parameters ◽  
Fiber Assembly ◽  
Yarn Quality ◽  
Spun Yarn ◽  
Ring Spun Yarn ◽  
Spinning Triangle ◽  
The Individual

The aim of the present work is to optimize the drafting parameters for ring spinning by using full factorial (23) experimental design. Three drafting parameters of ring spinning each at two levels were chosen for this study. These technological parameters were break draft, size of pin spacer and hardness of rubber cots. It was found from statistical analysis that pin spacer size has a significant effect on yarn unevenness (U %), imperfection index (IPI), hairiness (H) and yarn strength (CLSP) compared to the other two chosen factors. These yarn quality parameters were improved by increasing the spacer size. The increase in spacer size reduces the cohesive forces among the fibers during drafting. The pin between the cradle and the top front roller transfer the individual fibers from the drafted fiber assembly to the spinning triangle without any stretching or accumulation. This yields a more integrated structure and the quality of yarn is improved.

scholarly journals Regressional Optimizations of Cotton Spun Yarn by Controlling its Process Parameters

2021 ◽  
Author(s):  
Muhammad Ali Zeeshan ◽  
Zamir Ahmed Abro ◽  
Abdul Malik Rehan ◽  
Ahmer Hussain Shah ◽  
Nazakat Ali Khoso ◽  
...  
Keyword(s):  
Process Parameters ◽  
Natural Fiber ◽  
Linear Regression Analysis ◽  
Spindle Speed ◽  
Yarn Quality ◽  
Yarn Diameter ◽  
Yarn Twist ◽  
Spun Yarn ◽  
Ring Spun Yarn

Abstract Cotton is the most commonly used natural fiber and has a significant contribution to the production of yarn manufacturing. This yarn is subsequently utilized for the production of fabrics, garments, and other textile products. The quality of the end product depends on the selection of an appropriate spinning process and output parameters. Numerous methods and processes are involved in the production of yarn. Ring spinning machine is most commonly used for the production of cotton spun yarn. It is necessary to optimize the process parameters of ring-spun yarn without compromising on quality and production. In this research work; these parameters have been optimized by applying the multiple linear regression analysis. The process parameters (especially spindle speed, twist and yarn diameter) and their effect on yarn quality have been discussed in detail. Total 135 ring-spun yarn samples have been produced under three different levels of spindle speed, twist, and linear density. These yarn samples are categorized as 8 Ne, 16 Ne, and 24 Ne at three different Twist multipliers (3.8, 4.0, and 4.2) and different revolutions per minute of the spindle (9500 rpm, 10500rpm, and 11500 rpm). The models have been designed to predict the quality of ring-spun by utilizing USTER evenness tester data. The Count of yarn, yarn twist, and spindle speed were selected as a predictor. The multiple regression method has been used to find out the relation between the process parameters and yarn quality characteristics. The high values of R2 (the coefficient of determination) showed the relationships in the prediction model.

scholarly journals Quality Evaluation of Ethiopian 100% Cotton Carded Ring Spun Yarn with Respect to USTER Standards

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Biruk Fentahun Adamu ◽  
Desalegn Atalie ◽  
Erkihun Zelalem Liyew
Keyword(s):  
Tensile Properties ◽  
Research Work ◽  
Export Market ◽  
Yarn Strength ◽  
Yarn Quality ◽  
Spun Yarn ◽  
Spun Yarns ◽  
Ring Spun Yarn ◽  
Yarn Tenacity ◽  
Fabric Production

Yarn quality influences both fabric production processes efficiency and export market. One method used to gauge competitiveness of an industry is to study its product quality. The aim of this research work is to evaluate the quality of Ethiopian textile spinning mills’ 100% cotton carded ring spun yarns in terms of its evenness (coefficient of mass variation, CVm), imperfections (thick and thin places, neps), and tensile properties with USTER Statistics 2018. Five spinning mills (B3, A0, A2, A4, and K3) of 15N, 20Ne, 25Ne, 30Ne, 35Ne, and 40Ne nominal yarn counts have been selected for the study. The yarn evenness and imperfections were measured using USTER tester 5 and tensile using a STATIMAT tester. The USTER statistical results showed 20.3Ne (mill B3), 32Ne (mill A4), and 36.2Ne (mill A2) had better overall quality, respectively. It was observed that most selected spinning mills had low evenness, imperfections, yarn strength, and good yarn elongation. Tensile properties of A2 (32.85Ne and 36.2Ne) had fallen under 5% USTER statistics percentile which indicates excellent yarn strength. Generally, from studied mills, it was seen that 61.5% of cotton yarn CVm and thin places falls at above 95% and 15% of yarn tenacity falls at ≤5% of Uster statistical percentile.

A new method of yarn hairiness reduction by air suction during carding

2012 ◽  
Vol 82 (20) ◽  
pp. 2128-2136 ◽  
Author(s):  
Majid Mirzaei ◽  
Ali Akbar Gharehaghaji ◽  
Mohammad Zarrebini
Keyword(s):  
New Method ◽  
Short Fibers ◽  
Suction System ◽  
Elongation At Break ◽  
Yarn Quality ◽  
Spun Yarn ◽  
Yarn Properties ◽  
Ring Spun Yarn ◽  
Yarn Hairiness ◽  
Comparison Of The Results

Yarn hairiness has remained an issue of enormous interest in the field of yarn spinning research, since it directly affects yarn quality. In this work, a new method for the reduction of yarn hairiness is presented by attaching a simple effective air suction system to the web detaching zone of a conventional carding machine immediately behind crushing rollers. The slivers produced were almost free from dust or short loose fibers. Yarn properties such as hairiness, tenacity, elongation at break and evenness were evaluated. The ring-spun yarn that was produced was called Vacuum Cleaned Carded yarn or VCC yarn, due to the removal of the short fibers by air suction. The properties of VCC yarns were compared with those of conventionally produced reference yarn sample. Comparison of the results showed that the hairiness of optimum VCC yarn decreases by approximately 20%, while its tenacity, elongation at break and evenness were significantly improved. It was also found that the VCC yarn exhibited better spinning stability and was more environmentally friendly than the reference yarn.

Ring yarn quality prediction using hybrid artificial neural network

2015 ◽  
Vol 27 (6) ◽  
pp. 940-956 ◽  
Author(s):  
Hanen Ghanmi ◽  
Adel Ghith ◽  
Tarek Benameur
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Hybrid Model ◽  
Quality Index ◽  
Content Type ◽  
Yarn Quality ◽  
Spun Yarn ◽  
Yarn Properties ◽  
Ring Spun Yarn ◽  
Artificial Neural

Purpose – The purpose of this paper is to predict a global quality index of a ring spun yarn whose count Ne is ranging between 7.8 (76.92 tex) and 22.2 (27 tex). To fulfill this goal, a hybrid model based on artificial neural network (ANN) and fuzzy logic has been established. Fiber properties, yarn count and twist level are used as inputs to train the hybrid model and the output would be a quality index which includes the major physical properties of ring spun yarn. Design/methodology/approach – The hybrid model has been developed by means of the application of two soft computing approaches. These techniques are ANN which allows the authors to predict four important yarn properties, namely: tenacity, breaking elongation, unevenness and hairiness and fuzzy expert system which investigates spinner experience to give each combination of the four yarn properties an index ranging from 0 to 1. The prediction of the model accuracy was estimated using statistical performance criteria. These criteria are correlation coefficient, root mean square error, mean absolute error and mean relative percent error. Findings – The obtained results show that the constructed hybrid model is able to predict yarn quality from the chosen input variables with a reasonable degree of accuracy. Originality/value – Until now, there is no sufficiently information to evaluate and predict the global yarn quality from raw materials characteristics and process parameters. Therefore, this present paper’s aim is to investigate spinner experience and their understanding about both the impact of various parameters on yarn properties and the relationship between these properties and the global yarn quality to predict a quality index.

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