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Liu J.,Jiangnan University | Liu J.,National Engineering Research Center for Nonwovens | Liu X.,Jiangnan University | Xu Y.,Soochow University of China | Bao W.,Wuxi Entry Exit Inspection and Quarantine Bureau
Journal of the Textile Institute | Year: 2014

The normal incidence sound absorption coefficient of single-layered porous materials predicted using some prediction models is well known. The published acoustic behaviors prediction models, such as Biot model, Zwikker and Kosten model, Delany and Bazley model, and Champoux and Allard model, can give acceptable prediction results by only taking specific flow resistivity and material thickness as independent variables to estimate the normal incidence sound absorption coefficient. However, the existing literature fails to provide proper knowledge regarding the acoustic characteristics of dual-layered porous nonwoven absorbers. So, the aim of this paper was to propose a theoretical acoustic model for dual-layered porous nonwoven absorber and to verify the proposed model experimentally. In theory aspect, the study focused on the extension algorithm of the Zwikker and Kosten model for dual-layered nonwoven absorber. The theoretical analysis of the impact of thickness and porosity of outer and inner layer on sound absorption coefficient was detailed using numerical simulation method. In experiment aspect, we particularly designed 20 dual-layered nonwoven absorbers with four types of meltblown polypropylene nonwoven materials and five types of hydroentangled E-glass fiber nonwoven materials firstly. Secondly, the calculated sound absorption coefficients using the proposed model were compared with the measured ones of the 20 dual-layered nonwoven absorbers at 250, 500, 1000, and 2000 Hz. Experimental results indicate that the measured and the calculated data have very similar trend with the change of thickness, porosity, and the sound frequency, apart from the obvious difference between them at low frequency. © 2014 © 2014 The Textile Institute.


Liu J.,Jiangnan University | Liu J.,National Engineering Research Center for Nonwovens | Zuo B.,Soochow University of China | Zeng X.,University of Lille Nord de France | And 6 more authors.
Fibers and Polymers | Year: 2011

Previous work has shown that the uniformity recognition of nonwoven can be considered as a special case of pattern recognition. In this paper, a generalized frame for uniformity recognition based on computer vision and pattern recognition is introduced briefly. To validate the proposed generalized frame, a case study id carried out in experiment. In the experiment section, the uniformity recognition of nonwovens will be solved by unifying wavelet texture analysis, generalized Gaussian density (GGD) model and learning vector quantization (LVQ) neural network. 625 nonwoven images of 5 different uniformity grades, 125 of each grade, are decomposed at four levels with five different wavelet bases of Symlets family. And wavelet coefficients in each subband are independently modeled by the GGD model, while the scale and shape parameters of GGD model are extracted using maximum likelihood (ML) estimator as features to train and test LVQ neural network. For comparison, two energy-based features are also extracted from wavelet coefficients directly and jointly used as textural features. Experimental results coming from 625 nonwoven samples indicate the GGD parameters are more expressive and powerful in characterizing textures than the energy-based ones, especially when the number of decomposition levels is 4. © 2011 The Korean Fiber Society and Springer Netherlands.


Liu J.,Jiangnan University | Liu J.,Donghua University | Liu J.,National Engineering Research Center for Nonwovens | Bao W.,Wuxi Entry Exit Inspection and Quarantine Bureau | And 3 more authors.
Applied Acoustics | Year: 2014

In this paper, we propose a more general forecasting method to predict the sound absorption coefficients at six central frequencies and the average sound absorption coefficient of a sandwich structure nonwoven absorber. The kernel assumption of the proposed method is that the acoustics property of sandwich structure nonwoven absorber is determined by some easily measured structural parameters, such as thickness, area density, porosity, and pore size of each layer, if the type of the fiber used in nonwoven is given. By holding this assumption in mind, we will use general regression neural network (GRNN) as a prediction model to bridge the gap between the measured structural parameters of each absorber and its sound absorption coefficient. In experiment section, one hundred sandwich structure nonwoven absorbers are particularly designed with ten different types of meltblown polypropylene nonwoven materials and four types of hydroentangled E-glass fiber nonwoven materials firstly. Secondly, four structural parameters, i.e., thickness, area density, porosity, and pore size of each layer are instrumentally measured, which will be used as the inputs of GRNN. Thirdly, the sound absorption coefficients of each absorber are measured with SW477 impedance tube. The sound absorption coefficient at 125 Hz, 250 Hz, 500 Hz, 1000 Hz, 2000 Hz, 4000 Hz and their average value are used as the outputs of GRNN. Finally, the prediction framework will be carried out after the desired training set selection and spread parameter optimization of GRNN. The prediction results of 20 test samples show the prediction method proposed in this paper is reliable and efficient. © 2013 Elsevier Ltd. All rights reserved.


Liu J.,Jiangnan University | Liu J.,National Engineering Research Center for Nonwovens | Zuo B.,Soochow University of China | Gao W.,Jiangnan University
Acoustics Australia | Year: 2015

We propose to use general regression neural network (GRNN) to forecast the noise reduction ratio of a sandwich structure nonwoven absorber that bypasses the complex and heavy computation, which is more general compared with the models introduced in theoretical acoustics. The GRNN takes some easily measured structural parameters, such as thickness, area density, porosity, and pore size of each layer as inputs. The noise reduction ratio of each absorber is used as the GRNN's output. In experiment, one hundred sandwich structure nonwoven absorbers are particularly made of ten different types of meltblown polypropylene nonwoven materials and four types of hydroentangled E-glass fiber nonwovens initially. For comparison, the prediction model using back-propagation neural network is also built. The experiment results indicate that the prediction of noise reduction ratio using neural network-based method is reliable and efficient. © 2015 Australian Acoustical Society.


Liu J.,Jiangnan University | Liu J.,Donghua University | Liu J.,National Engineering Research Center for Nonwovens | Bao W.,Wuxi Entry Exit Inspection and Quarantine Bureau | And 5 more authors.
Noise Control Engineering Journal | Year: 2013

Predicting the acoustical behavior of noise control elements made of fibrous materials, especially the ones comprised by two types of nonwoven materials with different structural parameters, is a relevant topic in large spaces such as gymnasiums, cinemas, shopping malls, airports and stations. In this paper, we propose a more general prediction method for the noise reduction coefficient (NRC) of the dual layered nonwoven absorber by employing general regression neural network (GRNN). In the experiment section, fifty dual layered nonwoven absorbers are specifically designed by five different melt blown polypropylene and hydroentangled E-glass fiber nonwoven materials. Four structural parameters including thickness, area density, porosity, and pore size of each layer are measured, which are used as the inputs of GRNN. The sound absorption coefficients of each absorber are measured with SW477 impedance tube from 80 to 6300 Hz. The sound absorption average at 250, 500, 1000 and 2000 Hz is used to represent the NRC, which is also considered as an output of GRNN at prediction stage. Finally, the prediction framework is carried out after the desired training set selection and spread parameter optimization of GRNN. The prediction results of 10 test samples show that the prediction method proposed in this paper is reliable and efficient. © 2013 Institute of Noise Control Engineering.

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