Results The population had large variations for lint yield,fibre properties,predicted yarn properties,and composite fibre quality values.Lint yield with all fibre quality traits was not correlated.When the selection w...Results The population had large variations for lint yield,fibre properties,predicted yarn properties,and composite fibre quality values.Lint yield with all fibre quality traits was not correlated.When the selection was conducted first to keep those with improved fibre quality,and followed for high yields,a large proportion in the resultant populations was the same between selections based on Cottonspec predicted yarn quality and HVI-measured fibre properties.They both exceeded the selection based on FQI and Background The approach of directly testing yarn quality to define fibre quality breeding objectives and progress the selection is attractive but difficult when considering the need for time and labour.The question remains whether yarn prediction tools from textile research can serve as an alternative.In this study,using a dataset from three seasons of field testing recombinant inbred line population,Cottonspec,a software developed by the Commonwealth Scientific and Industrial Research Organisation(CSIRO)for predicting ring spun yarn quality from fibre properties measured by High Volume Instrument(HVI),was used to select improved fibre quality and lint yield in the population.The population was derived from an advanced generation inter-crossing of four CSIRO conventional commercial varieties.The Cottonspec program was able to provide an integrated index of the fibre qualities affecting yarn properties.That was compared with selection based on HVI-measured fibre properties,and two composite fibre quality variables,namely,fibre quality index(FQI),and premium and discount(PD)points.The latter represents the net points of fibre length,strength,and micronaire based on the Premiums and Discounts Schedule used in the market while modified by the inclusion of elongation.PD points.Conclusions The population contained elite segregants with improved yield and fibre properties,and Cottonspec predicted yarn quality is useful to effectively capture these elites.There is a need to further develop yarn quality prediction tools through collaborative efforts with textile mills,to draw better connectedness between fibre and yarn quality.This connection will support the entire cotton value chain research and evolution.展开更多
In order to solve the problems of weak prediction stability and generalization ability of a neural network algorithm model in the yarn quality prediction research for small samples,a prediction model based on an AdaBo...In order to solve the problems of weak prediction stability and generalization ability of a neural network algorithm model in the yarn quality prediction research for small samples,a prediction model based on an AdaBoost algorithm(AdaBoost model) was established.A prediction model based on a linear regression algorithm(LR model) and a prediction model based on a multi-layer perceptron neural network algorithm(MLP model) were established for comparison.The prediction experiments of the yarn evenness and the yarn strength were implemented.Determination coefficients and prediction errors were used to evaluate the prediction accuracy of these models,and the K-fold cross validation was used to evaluate the generalization ability of these models.In the prediction experiments,the determination coefficient of the yarn evenness prediction result of the AdaBoost model is 76% and 87% higher than that of the LR model and the MLP model,respectively.The determination coefficient of the yarn strength prediction result of the AdaBoost model is slightly higher than that of the other two models.Considering that the yarn evenness dataset has a weaker linear relationship with the cotton dataset than that of the yarn strength dataset in this paper,the AdaBoost model has the best adaptability for the nonlinear dataset among the three models.In addition,the AdaBoost model shows generally better results in the cross-validation experiments and the series of prediction experiments at eight different training set sample sizes.It is proved that the AdaBoost model not only has good prediction accuracy but also has good prediction stability and generalization ability for small samples.展开更多
In the spinning process, some key process parameters( i. e.,raw material index inputs) have very strong relationship with the quality of finished products. The abnormal changes of these process parameters could result...In the spinning process, some key process parameters( i. e.,raw material index inputs) have very strong relationship with the quality of finished products. The abnormal changes of these process parameters could result in various categories of faulty products. In this paper, a hybrid learning-based model was developed for on-line intelligent monitoring and diagnosis of the spinning process. In the proposed model, a knowledge-based artificial neural network( KBANN) was developed for monitoring the spinning process and recognizing faulty quality categories of yarn. In addition,a rough set( RS)-based rule extraction approach named RSRule was developed to discover the causal relationship between textile parameters and yarn quality. These extracted rules were applied in diagnosis of the spinning process, provided guidelines on improving yarn quality,and were used to construct KBANN. Experiments show that the proposed model significantly improve the learning efficiency, and its prediction precision is improved by about 5. 4% compared with the BP neural network model.展开更多
In this paper, the spinning parameters are optimized by using the method of factor analysis. The yarns obtained from four different spinning parameters are evaluated by this method. Two common factors, fineness uneven...In this paper, the spinning parameters are optimized by using the method of factor analysis. The yarns obtained from four different spinning parameters are evaluated by this method. Two common factors, fineness unevenness and tenacity level, are extracted from the seven yarn-quality indexes. The accumulative contribution percentage of the two factors is up to 91.813%,and much information in the yarn-quality indexes is reflected by the two factors. Then the score of each factor is calculated to evaluate the quality of yarn. Based on that, the techniques are optimized. The result is well in line with spinning practices, so it is testified feasibly to use this method to optimize spinning parameter.展开更多
Although many works have been done to construct prediction models on yarn processing quality,the relation between spinning variables and yarn properties has not been established conclusively so far.Support vector mach...Although many works have been done to construct prediction models on yarn processing quality,the relation between spinning variables and yarn properties has not been established conclusively so far.Support vector machines(SVMs),based on statistical learning theory,are gaining applications in the areas of machine learning and pattern recognition because of the high accuracy and good generalization capability.This study briefly introduces the SVM regression algorithms,and presents the SVM based system architecture for predicting yarn properties.Model selection which amounts to search in hyper-parameter space is performed for study of suitable parameters with grid-research method.Experimental results have been compared with those of artificial neural network(ANN)models.The investigation indicates that in the small data sets and real-life production,SVM models are capable of remaining the stability of predictive accuracy,and more suitable for noisy and dynamic spinning process.展开更多
The Coefficient of Variation(CV)of hectometer yarn's weight is one of the guidelines to evaluate its intrinsic quality.In the spinning manufacturing,the control of cotton yarn's weight unevenness is accomplish...The Coefficient of Variation(CV)of hectometer yarn's weight is one of the guidelines to evaluate its intrinsic quality.In the spinning manufacturing,the control of cotton yarn's weight unevenness is accomplished mainly in terms of a spot-check on semi-product and a succedent adjust in process parameters during spinning based on technicians' experience.However,it is theoretically believed among manufacturers that with fixed technical levels and parameters in the spinning process,the quality parameters of assorted cotton have a certain influence on the CV.In order to find out a rule of the influence that assorted cotton has on the CV,a GM(1,N)model,correlated raw cotton's quality parameter with the CV,has firstly been developed according to the modeling theory of grey system,and then been applied in the designing step to predict the CV.It has been approved by practical modeling and validation that the model could fit preferably an accrual CV value,and provide a method of quantitative predicting analysis for textile manufacturers to design cotton yarn's quality.展开更多
In previous research much effort has been devoted to the geometry of woven fabrics and relat-ed problems under the assumption of constant yarn configuration in fabric.This paper will first re-port that image crimp (ya...In previous research much effort has been devoted to the geometry of woven fabrics and relat-ed problems under the assumption of constant yarn configuration in fabric.This paper will first re-port that image crimp (yarn crimp measured by an image analysis method) seems larger than actualvalue.From the explanation of this result,the variation of yarn configuration in woven fabric dueto the non-uniform flattening is revealed.The significance of this actual structure of woven fabricsis discussed.It is believed that the variation of yarn configuration is very important for fabric per-formance,and may be an advantage for fabric 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 technique of sirospun process is applied on a modified semi-worsted balloonless spinningframe to investigate the effect of spindle-speed,yarn-twist and strand-spacing on yarn properties.Yarn breaking strength,brea...The technique of sirospun process is applied on a modified semi-worsted balloonless spinningframe to investigate the effect of spindle-speed,yarn-twist and strand-spacing on yarn properties.Yarn breaking strength,breaking extension,evenness and imperfection are examined on the basisof CCD experimental design.Yarn hairiness is particularly concerned,being found that all spinningparameters tested have significant effects on hairiness and that the minimum number of hairs oc-curs at the strand-spacing of 14.4 mm.Compared to conventional single spun yarn,experimentshave revealed the greatest advantage of using sirospun process is that all sirospun yarns have muchless hairiness.A new sirospun yarn fault,so called“loop”,has also been examined.The most likely cause forthis yarn fault is the strand-tension unbalance between the two strands when low tension spinningis applied.Further analysis and some initial tests have been carried out in the hope of overcomingthis loop fault which is an important obstacle to the application of balloonless spinning.展开更多
At present, we have succeeded in producing silk noil yarn by rotor spinning, and obtained good economic benefits. In this paper, through combining with the recent producing practice, systematically discussing and anal...At present, we have succeeded in producing silk noil yarn by rotor spinning, and obtained good economic benefits. In this paper, through combining with the recent producing practice, systematically discussing and analysing the technology in the process of producing silk noil yarn by rotor spinning, we develop the new technology which is suited to produce silk noil yarn by rotor spinning, and address several noticable problems. The conclusions have been put into practice and proved to be effective and reliable. It can be consulted by the textile mills.展开更多
This article is devoted to the research work aimed at improving the quality of yarns obtained by pneumomechanical spinning. The yarn quality indicators obtained at different speed modes of the pneumomechanical spinnin...This article is devoted to the research work aimed at improving the quality of yarns obtained by pneumomechanical spinning. The yarn quality indicators obtained at different speed modes of the pneumomechanical spinning machine discrete drum were studied and analyzed. The effect of the number of incisions of the sawtooth coatings on the discrete drum on the quality indicators of the yarn produced was also studied. The results of the experiments were analyzed by graphical and histogram methods, and alternative options were suggested.展开更多
基金funded through Cotton Breeding Australia,a Joint Venture between CSIRO and Cotton Seed Distributors(Wee Waa,NSW 2388,Australia)。
文摘Results The population had large variations for lint yield,fibre properties,predicted yarn properties,and composite fibre quality values.Lint yield with all fibre quality traits was not correlated.When the selection was conducted first to keep those with improved fibre quality,and followed for high yields,a large proportion in the resultant populations was the same between selections based on Cottonspec predicted yarn quality and HVI-measured fibre properties.They both exceeded the selection based on FQI and Background The approach of directly testing yarn quality to define fibre quality breeding objectives and progress the selection is attractive but difficult when considering the need for time and labour.The question remains whether yarn prediction tools from textile research can serve as an alternative.In this study,using a dataset from three seasons of field testing recombinant inbred line population,Cottonspec,a software developed by the Commonwealth Scientific and Industrial Research Organisation(CSIRO)for predicting ring spun yarn quality from fibre properties measured by High Volume Instrument(HVI),was used to select improved fibre quality and lint yield in the population.The population was derived from an advanced generation inter-crossing of four CSIRO conventional commercial varieties.The Cottonspec program was able to provide an integrated index of the fibre qualities affecting yarn properties.That was compared with selection based on HVI-measured fibre properties,and two composite fibre quality variables,namely,fibre quality index(FQI),and premium and discount(PD)points.The latter represents the net points of fibre length,strength,and micronaire based on the Premiums and Discounts Schedule used in the market while modified by the inclusion of elongation.PD points.Conclusions The population contained elite segregants with improved yield and fibre properties,and Cottonspec predicted yarn quality is useful to effectively capture these elites.There is a need to further develop yarn quality prediction tools through collaborative efforts with textile mills,to draw better connectedness between fibre and yarn quality.This connection will support the entire cotton value chain research and evolution.
文摘In order to solve the problems of weak prediction stability and generalization ability of a neural network algorithm model in the yarn quality prediction research for small samples,a prediction model based on an AdaBoost algorithm(AdaBoost model) was established.A prediction model based on a linear regression algorithm(LR model) and a prediction model based on a multi-layer perceptron neural network algorithm(MLP model) were established for comparison.The prediction experiments of the yarn evenness and the yarn strength were implemented.Determination coefficients and prediction errors were used to evaluate the prediction accuracy of these models,and the K-fold cross validation was used to evaluate the generalization ability of these models.In the prediction experiments,the determination coefficient of the yarn evenness prediction result of the AdaBoost model is 76% and 87% higher than that of the LR model and the MLP model,respectively.The determination coefficient of the yarn strength prediction result of the AdaBoost model is slightly higher than that of the other two models.Considering that the yarn evenness dataset has a weaker linear relationship with the cotton dataset than that of the yarn strength dataset in this paper,the AdaBoost model has the best adaptability for the nonlinear dataset among the three models.In addition,the AdaBoost model shows generally better results in the cross-validation experiments and the series of prediction experiments at eight different training set sample sizes.It is proved that the AdaBoost model not only has good prediction accuracy but also has good prediction stability and generalization ability for small samples.
基金National Natural Science Foundation of China(No.51175077)
文摘In the spinning process, some key process parameters( i. e.,raw material index inputs) have very strong relationship with the quality of finished products. The abnormal changes of these process parameters could result in various categories of faulty products. In this paper, a hybrid learning-based model was developed for on-line intelligent monitoring and diagnosis of the spinning process. In the proposed model, a knowledge-based artificial neural network( KBANN) was developed for monitoring the spinning process and recognizing faulty quality categories of yarn. In addition,a rough set( RS)-based rule extraction approach named RSRule was developed to discover the causal relationship between textile parameters and yarn quality. These extracted rules were applied in diagnosis of the spinning process, provided guidelines on improving yarn quality,and were used to construct KBANN. Experiments show that the proposed model significantly improve the learning efficiency, and its prediction precision is improved by about 5. 4% compared with the BP neural network model.
文摘In this paper, the spinning parameters are optimized by using the method of factor analysis. The yarns obtained from four different spinning parameters are evaluated by this method. Two common factors, fineness unevenness and tenacity level, are extracted from the seven yarn-quality indexes. The accumulative contribution percentage of the two factors is up to 91.813%,and much information in the yarn-quality indexes is reflected by the two factors. Then the score of each factor is calculated to evaluate the quality of yarn. Based on that, the techniques are optimized. The result is well in line with spinning practices, so it is testified feasibly to use this method to optimize spinning parameter.
基金National Science Foundation and Technology Innovation Fund of P.R.China(No.70371040and02LJ-14-05-01)
文摘Although many works have been done to construct prediction models on yarn processing quality,the relation between spinning variables and yarn properties has not been established conclusively so far.Support vector machines(SVMs),based on statistical learning theory,are gaining applications in the areas of machine learning and pattern recognition because of the high accuracy and good generalization capability.This study briefly introduces the SVM regression algorithms,and presents the SVM based system architecture for predicting yarn properties.Model selection which amounts to search in hyper-parameter space is performed for study of suitable parameters with grid-research method.Experimental results have been compared with those of artificial neural network(ANN)models.The investigation indicates that in the small data sets and real-life production,SVM models are capable of remaining the stability of predictive accuracy,and more suitable for noisy and dynamic spinning process.
基金Hunan Provincial Basic Science Foundation of China(No.2007FJ3046)Key Scientific Research Fundof Hunan Provincial Education Department,China(No.07A048)
文摘The Coefficient of Variation(CV)of hectometer yarn's weight is one of the guidelines to evaluate its intrinsic quality.In the spinning manufacturing,the control of cotton yarn's weight unevenness is accomplished mainly in terms of a spot-check on semi-product and a succedent adjust in process parameters during spinning based on technicians' experience.However,it is theoretically believed among manufacturers that with fixed technical levels and parameters in the spinning process,the quality parameters of assorted cotton have a certain influence on the CV.In order to find out a rule of the influence that assorted cotton has on the CV,a GM(1,N)model,correlated raw cotton's quality parameter with the CV,has firstly been developed according to the modeling theory of grey system,and then been applied in the designing step to predict the CV.It has been approved by practical modeling and validation that the model could fit preferably an accrual CV value,and provide a method of quantitative predicting analysis for textile manufacturers to design cotton yarn's quality.
文摘In previous research much effort has been devoted to the geometry of woven fabrics and relat-ed problems under the assumption of constant yarn configuration in fabric.This paper will first re-port that image crimp (yarn crimp measured by an image analysis method) seems larger than actualvalue.From the explanation of this result,the variation of yarn configuration in woven fabric dueto the non-uniform flattening is revealed.The significance of this actual structure of woven fabricsis discussed.It is believed that the variation of yarn configuration is very important for fabric per-formance,and may be an advantage for fabric 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 technique of sirospun process is applied on a modified semi-worsted balloonless spinningframe to investigate the effect of spindle-speed,yarn-twist and strand-spacing on yarn properties.Yarn breaking strength,breaking extension,evenness and imperfection are examined on the basisof CCD experimental design.Yarn hairiness is particularly concerned,being found that all spinningparameters tested have significant effects on hairiness and that the minimum number of hairs oc-curs at the strand-spacing of 14.4 mm.Compared to conventional single spun yarn,experimentshave revealed the greatest advantage of using sirospun process is that all sirospun yarns have muchless hairiness.A new sirospun yarn fault,so called“loop”,has also been examined.The most likely cause forthis yarn fault is the strand-tension unbalance between the two strands when low tension spinningis applied.Further analysis and some initial tests have been carried out in the hope of overcomingthis loop fault which is an important obstacle to the application of balloonless spinning.
文摘At present, we have succeeded in producing silk noil yarn by rotor spinning, and obtained good economic benefits. In this paper, through combining with the recent producing practice, systematically discussing and analysing the technology in the process of producing silk noil yarn by rotor spinning, we develop the new technology which is suited to produce silk noil yarn by rotor spinning, and address several noticable problems. The conclusions have been put into practice and proved to be effective and reliable. It can be consulted by the textile mills.
文摘This article is devoted to the research work aimed at improving the quality of yarns obtained by pneumomechanical spinning. The yarn quality indicators obtained at different speed modes of the pneumomechanical spinning machine discrete drum were studied and analyzed. The effect of the number of incisions of the sawtooth coatings on the discrete drum on the quality indicators of the yarn produced was also studied. The results of the experiments were analyzed by graphical and histogram methods, and alternative options were suggested.