The Prediction Of Worsted Yarn Quality Based On Computational Intelligence

The spun yarn quality has great effect on the efficiency of the sequential weaving process and the quality of the final produced fabric in textile production.The traditional method of yarn quality control is phase lag control,i.e.the yarn quality informations are only available after the yarn has been spun and tested.In this case, the mill will be incapable of doing anything to retrieve the results,if the yarn is found to be unqualified.It has become a matter of great concern in spinning mill nowadays that how to predict yarn quality before the yarn spinning process starts.The tool for resolving this problem is to find out a model which relates the yarn quality index to input material properties,spinning parameters and yarn specifications,and can be used for the prediction of yarn quality' before the yarn to be spun.With the aid of this model,the spinner will be able to do the virtual(simulation)spinning and make the yarn quality required by adjusting the input material's specifications and spinning parameters.Obviously,such a model as mentioned above must be the one which is characteristic of complicated non-linear and capable of self-adaptively adjusting to fitting the dynamic variation of the spinning.The traditional methods of physical/mathematical modeling and statistical regression modeling are,therefore, unsatisfied due to the limitations possessed themselves respectively.It is necessary to use the modern modeling method for efficiently predicting the yarn quality.Since the 90's of the last century,the Computational Intelligent(CI)which originates from the simulation of biological system and its behavior feature has been employed in some areas of science and technology.Artificial Neural Network(ANN), Genetic Algorithm(GA)and Genetic Programming(GP)are 3 branches of CI and also have been reported to be applied to products inspection,process control and quality prediction in textile researches area.These modeling methods are capable of not only overcoming the disadvantages of traditional physical/mathematical models and statistical regression methods,but also self-adaptively-adjusting to fit the dynamic variation of yarn production.This thesis will apply these modeling methods to predict the worsted yarn quality.The thesis summarizes,at first,the former achievements of yarn quality prediction based on the physical/mathematical and statistical regression models,so as to draw the factors affecting the yarn quality.Then the new models for predicting the worsted yarn quality will be designed by using ANN,GA,GP and their combination. In the designed new models,the factors drawn will be taken as the input variables. The thesis consists of a preface and seven chapters.The details are as following:The preface gives a brief introduction of Computational Intelligence and the background of the research topic of this thesis to be chosen.The first chapter is presented as the literature review of the current research status of applying ANN,GA and GP to quality prediction,fiber & fabric identification, weaving defect detecting,apparel hand feeling evaluation,worsted fabric expert system and etc.In the chapter,the main research contents of the thesis are put forwarded according to the gaps of the former research work.In the second chapter,the former research achievements of yarn quality prediction based on physical/mathematical and statistical regression models are overviewed and summarized.The parameters are drawn from these models,thereby, all of which have meaningful affects to yarn unevenness,thin places,thick places, neps,yarn tenacity and its variation,yarn elongation at breakage,ends-down during spinning.These parameters will be also taken as the input variables of the models designed in the following chapters.In the third chapter,8 prediction models are built up for predicting the yarn evenness,thin places,thick places,neps,yarn tenacity and its variation,yarn elongation at breakage,ends-down during spinning respectively.The models are the construction of Multi-Layer Perceptron(MLP)with multiple inputs,single output and one hidden layer,and trained by using Levenberg-Marquardt(LM)learning algorithm. The affective parameters summed in chapter 2 are taken as the input variables of the models in this chapter.The prediction results with good accuracy for the yarn unevenness,thin places and thick places are obtained,in which the square of correlation coefficient(R~2)between the measured values and predicted values is 0.9408,0.9713 and 0.8930 respectively.The prediction results for yarn tenacity and its variation,and elongation at breakage are not so good as above,the R~2 values are 0.7930,0.8082 and 0.8331 respectively.For neps and ends-down during spinning,the R~2 values are poor,only 0.6132 and 0.6670 respectively.These results show that the models need to be further improved and optimized.In chapter 4,a detailed description of the MLP-GA prediction models is given.In the models,the designed master-slave multi-deme parallel genetic algorithm is used to improve the model's performance,in which,the master GA algorithm is used to optimize the construction of MLP,i.e.the input variables and the number of nodes in hidden layer,and the slave GA algorithm to optimize the initial parameters of MLP, i.e.the connecting weight and bias.in order to avoid the local optimum due to the incorrect initial parameters setting.The master-slave multi-deme parallel genetic algorithm makes the searching area of MLP construction and MLP initial parameters to be expanded into global solution space,thus the MLP performance is much improved and the prediction results are stable.It is shown that the R~2 values of yarn unevenness,thin places and thick places are similar to that of MLP model,i.e.0.9464, 0.9766 and 0.9177 respectively and the R~2 values of yarn tenacity and its variation, elongation at breakage,neps and ends-down during spinning are raised up to 0.9404, 0.9320,0.9412,0.8733 and 0.8977 respectively.In chapter 5,the wavelet MLP models(MLP-Wavelet)are used to predict yarn quality and spinning performance.In these models,the Sigmoid transfer function in MLP hidden layer is replaced by Morlet wavelet basis.The model is formed by the linear addition of the Morlet wavelet basis to fit the MLP output functions.The gradient descent algorithm is applied to train MLP-Wavelet model.The prediction results are superior to MLP model.The R~2 values for yarn unevenness,thin places, yarn tenacity,and yarn elongation are slightly better than that of MLP-GA models,but the R~2 values for neps index is lower than that of MLP-GA model.They are 0.9854, 0.9758,0.9312,0.9596,0.9284,0.9624,0.8474 and 0.9094 corresponding to yarn unevenness,thin places,thick places,yarn tenacity and its variation,elongation at breakage,neps and ends-down during spinning respectively.Compared to MLP-GA model,MLP-Wavelet model shows higher training efficiency,but lack of stability.In chapter 6,the GP model is applied to predict yam quality and spinning performance.After giving a short introduction of the principle and method of GP,the same train set as that in former chapters is used to build the genetic programmer and accordant empirical engineering formula.The results predicted by using the formula for yam unevenness,thin places and thick places are very close to measured values when inputting the same test set as that in former chapters.The R~2 values reach to 0.9451,0.9885 and 0.9357 respectively.Besides that,the formula is also capable of showing clearly the complex relationship between the yarn quality,spinning performance,and input material's properties,spinning parameters and yarn specifications.Chapter 7 gives out the conclusion and prospect.The main contributions of the paper are summed and an outline of the problems which need to be further studied is made as well.