This paper presents a comparison study of two models for predicting the strength of rotor spun cotton yarns from fiber properties. The adaptive neuro-fuzzy system inference （ANFIS） and Multiple Linear Regression mod...This paper presents a comparison study of two models for predicting the strength of rotor spun cotton yarns from fiber properties. The adaptive neuro-fuzzy system inference （ANFIS） and Multiple Linear Regression models are used to predict the rotor spun yarn strength. Fiber properties and yam count are used as inputs to train the two models and the count-strength-product （CSP） was the targel. The predictive performances of the two models are estimated and compared. We found that the ANFIS has a better predictive power in comparison with linear multiple regression model. The impact of each fiber property is also illustrated.展开更多
Yarn quality characteristics are affected by processing parameters. A 36 tex rotor spun yarn of 50/50 Basofil/ cotton （B/C） blended yarn was spun, and the spinning process optimised for rotor speed, opening roller s...Yarn quality characteristics are affected by processing parameters. A 36 tex rotor spun yarn of 50/50 Basofil/ cotton （B/C） blended yarn was spun, and the spinning process optimised for rotor speed, opening roller speed and twist factor. Selected yarn characteristics were studied during the optimization process. During the optimizations process yarn elongation and hairiness reduced with increase in rotor speed. Tenacity increased with increase of rotor speed. The increase in TF caused tenacity and CV of count to increase up to a peak and then started to decrease with further increase of TF.While TF caused an increase in yarn hairiness, elongation decreased to a minimum level and then started to increase with further increase of TF. CV of count and hairiness increased with increase in opening roller speed, but tenacity and elongation decreased with increase in opening roller speed. The optimization process yielded the optimum levels for rotor speed, opening roller speed and twist factor （TF） as 45,000 rpm, 6,500 rpm and 450 respectively. As per uster Standards the optimum yam showed good results for CV of count, CV of tenacity and thin places/km.展开更多
A data mining method for quality prediction using association rule (DMAR) is presented in this paper. Association rule is used to mine the valuable relations of items among amounts of textile process data for ANN pred...A data mining method for quality prediction using association rule (DMAR) is presented in this paper. Association rule is used to mine the valuable relations of items among amounts of textile process data for ANN prediction model. DMAR consists of three main steps: setup knowledge data set; data cleaning and converting; find the item set with large supports and generate the expected rules. DMAR effectively improves the precision of prediction in yarn breaking. It rapidly gets rid of the negative influence of training parameters on prediction model. Then more satisfactory quality prediction result can be reached.展开更多
Three-dimensional Rotational Microscopy was used to take the photos of the fabrics. Three categories of yarn appearance diameters in the worsted fabrics were discussed. The fabrics were grouped into two to explore the...Three-dimensional Rotational Microscopy was used to take the photos of the fabrics. Three categories of yarn appearance diameters in the worsted fabrics were discussed. The fabrics were grouped into two to explore the relationships between yam appearance diameters at the interlacing points, opening points, and the calculated yam diameters. The correlations between the appearance diameters and fabric parameters were given, and the results showed that the calculated yam diameter and warp cover factor had a very big influence on yam appearance diameter. The equations expressing the relationship between yam appearance diameters and fabric parameters are established using step-regression method. The validation of the equations for one type of fabrics shows a good accuracy with the average error below 9% except the weft fdaments exceeding 22%.展开更多
Discussed in the article are the factors for the imperfect quality of piecing based on the principles and the key technologies of semiautomatic piecing. Furthermore, three measures including changing the shape of seed...Discussed in the article are the factors for the imperfect quality of piecing based on the principles and the key technologies of semiautomatic piecing. Furthermore, three measures including changing the shape of seed yarn tail, changing the feed speed and removing the damaged fibers are put forward. Experimental results prove that the former two, especial the second one, offers better piecing quality.展开更多
Back-Propagation (BP) neural network and its modified algorithm are introduced. Two series of BP neural network models have been established to predict yarn properties and to deduce wool fiber qualities. The results f...Back-Propagation (BP) neural network and its modified algorithm are introduced. Two series of BP neural network models have been established to predict yarn properties and to deduce wool fiber qualities. The results from these two series of models have been compared with the measured values respectively, proving that the accuracy in both the prediction model and the deduction model is high. The experimental results and the corresponding analysis show that the BP neural network is an efficient technique for the quality prediction and has wide prospect in the application of worsted yarn production system.展开更多
The methods in testing the bean-protein fiber and the standards used were simply introduced. The fiber's mechanical and chemical performances were further analyzed. And the correlative performance of the bean-prot...The methods in testing the bean-protein fiber and the standards used were simply introduced. The fiber's mechanical and chemical performances were further analyzed. And the correlative performance of the bean-protein fibers and other natural fibers have been compared, then full knowledge of the fiber's performance was concluded.展开更多
In order to study the propulsive force on the water-jet to the flying weft in water-jet looms, a dynamic model has been established. Based on the analysis and example testing, an experiential formula of the propulsive...In order to study the propulsive force on the water-jet to the flying weft in water-jet looms, a dynamic model has been established. Based on the analysis and example testing, an experiential formula of the propulsive force of the water-jet to the flying weft is obtained for the first time. The formula will profit the further research of the water-jet weft insertion and the production of textile.展开更多
文摘This paper presents a comparison study of two models for predicting the strength of rotor spun cotton yarns from fiber properties. The adaptive neuro-fuzzy system inference （ANFIS） and Multiple Linear Regression models are used to predict the rotor spun yarn strength. Fiber properties and yam count are used as inputs to train the two models and the count-strength-product （CSP） was the targel. The predictive performances of the two models are estimated and compared. We found that the ANFIS has a better predictive power in comparison with linear multiple regression model. The impact of each fiber property is also illustrated.
文摘Yarn quality characteristics are affected by processing parameters. A 36 tex rotor spun yarn of 50/50 Basofil/ cotton （B/C） blended yarn was spun, and the spinning process optimised for rotor speed, opening roller speed and twist factor. Selected yarn characteristics were studied during the optimization process. During the optimizations process yarn elongation and hairiness reduced with increase in rotor speed. Tenacity increased with increase of rotor speed. The increase in TF caused tenacity and CV of count to increase up to a peak and then started to decrease with further increase of TF.While TF caused an increase in yarn hairiness, elongation decreased to a minimum level and then started to increase with further increase of TF. CV of count and hairiness increased with increase in opening roller speed, but tenacity and elongation decreased with increase in opening roller speed. The optimization process yielded the optimum levels for rotor speed, opening roller speed and twist factor （TF） as 45,000 rpm, 6,500 rpm and 450 respectively. As per uster Standards the optimum yam showed good results for CV of count, CV of tenacity and thin places/km.
文摘A data mining method for quality prediction using association rule (DMAR) is presented in this paper. Association rule is used to mine the valuable relations of items among amounts of textile process data for ANN prediction model. DMAR consists of three main steps: setup knowledge data set; data cleaning and converting; find the item set with large supports and generate the expected rules. DMAR effectively improves the precision of prediction in yarn breaking. It rapidly gets rid of the negative influence of training parameters on prediction model. Then more satisfactory quality prediction result can be reached.
文摘Three-dimensional Rotational Microscopy was used to take the photos of the fabrics. Three categories of yarn appearance diameters in the worsted fabrics were discussed. The fabrics were grouped into two to explore the relationships between yam appearance diameters at the interlacing points, opening points, and the calculated yam diameters. The correlations between the appearance diameters and fabric parameters were given, and the results showed that the calculated yam diameter and warp cover factor had a very big influence on yam appearance diameter. The equations expressing the relationship between yam appearance diameters and fabric parameters are established using step-regression method. The validation of the equations for one type of fabrics shows a good accuracy with the average error below 9% except the weft fdaments exceeding 22%.
文摘Discussed in the article are the factors for the imperfect quality of piecing based on the principles and the key technologies of semiautomatic piecing. Furthermore, three measures including changing the shape of seed yarn tail, changing the feed speed and removing the damaged fibers are put forward. Experimental results prove that the former two, especial the second one, offers better piecing quality.
文摘Back-Propagation (BP) neural network and its modified algorithm are introduced. Two series of BP neural network models have been established to predict yarn properties and to deduce wool fiber qualities. The results from these two series of models have been compared with the measured values respectively, proving that the accuracy in both the prediction model and the deduction model is high. The experimental results and the corresponding analysis show that the BP neural network is an efficient technique for the quality prediction and has wide prospect in the application of worsted yarn production system.
文摘The methods in testing the bean-protein fiber and the standards used were simply introduced. The fiber's mechanical and chemical performances were further analyzed. And the correlative performance of the bean-protein fibers and other natural fibers have been compared, then full knowledge of the fiber's performance was concluded.
文摘In order to study the propulsive force on the water-jet to the flying weft in water-jet looms, a dynamic model has been established. Based on the analysis and example testing, an experiential formula of the propulsive force of the water-jet to the flying weft is obtained for the first time. The formula will profit the further research of the water-jet weft insertion and the production of textile.
