针刺非织造材料形态与组合结构对材料吸声性能的影响

利用驻波管测试方法研究了几种不同厚度、不同密度和两种不同类型的纤维材料及其组合结构的吸声性能。研究结果表明,材料厚度的单因子条件与材料的吸声系数呈正线性相关;随着材料体积密度的增加,中低频声波的吸声系数提高,而高频声波的吸声系数呈现先提高后降低的趋势;材料的组合结构对材料的吸声性能影响很大,由中空纤维制成的低密度非织造材料与高密度非织造材料组合,并将高密度非织造材料置于表面,该组合方式的材料其吸声性能明显优于其他几种组合方式的材料。     

基于BP神经网络的非织造材料基复合吸声体吸声系数预测

以涤纶针刺非织造材料和聚丙烯熔喷非织造材料为研究对象,通过实验获得其物理结构参数,并将复合前后非织造材料厚度、面密度、孔隙率和孔径作为BP神经网络的输入项,用于预测吸声体的平均吸声系数,同时通过调节输入神经元个数、传递函数和隐含层个数构建了最佳的BP神经网络预测模型。对非织造材料基复合吸声体的吸声性能进行预测,并与测试结果进行了对比。结果表明,运用BP神经网络可以建立较理想的适用于复合吸声体平均吸声系数预测的模型。     

结构参数对非织造材料吸声系数的影响

分别以涤纶针刺法非织造材料和丙纶熔喷非织造材料为基,通过热粘合方式制成不同参数的同质非织造材料吸声体。通过分析同质非织造材料吸声体的厚度、面密度、孔径、孔隙率等结构参数与其平均吸声系数之间的关系,探讨非织造材料结构参数对其吸声性能的影响。实验结果表明,对于同种材料而言,涤纶针刺非织造材料与丙纶熔喷非织造材料的结构参数对其平均吸声系数具有较大影响,材料的平均吸声系数随厚度和面密度的增加而增加。     

Analysis of the effect of fiber cross section and different bonding methods on sound absorption performance of PET fiber based nonwovens using Taguchi method

Abstract

In this study, sound absorption coefficients (SACs) of needle-punched and thermal-bonded nonwovens produced from polyester (PET) fibers with various cross sections, i.e., hollow, round and hexaflower, blended with a low melt PET, was reported. The acoustic performance of a hexaflower PET fiber was studied for the first time. Fibers were carded and then one set of samples was bonded by needle punching while the other set was air-through thermal bonded. A third set of samples was needled at various punch densities. Design of experiments was planned according to Taguchi method. Relationship between production parameters and SAC was analyzed using Minitab software. The most important independent variables affecting the sound absorption were areal density and web bonding method. The sample produced according to optimum production levels reached to a SAC value of 0.57 at 2000?Hz which could be a suitable choice for acoustic applications in the automotive industry.     

A comparison of artificial neural networks and partial least squares modelling for the rapid detection of the microbial spoilage of beef fillets based on Fourier transform infrared spectral fingerprints

A series of partial least squares (PLS) models were employed to correlate spectral data from FTIR analysis with beef fillet spoilage during aerobic storage at different temperatures (0, 5, 10, 15, and 20 °C) using the dataset presented by Argyri et al. (2010). The performance of the PLS models was compared with a three-layer feed-forward artificial neural network (ANN) developed using the same dataset. FTIR spectra were collected from the surface of meat samples in parallel with microbiological analyses to enumerate total viable counts. Sensory evaluation was based on a three-point hedonic scale classifying meat samples as fresh, semi-fresh, and spoiled. The purpose of the modelling approach employed in this work was to classify beef samples in the respective quality class as well as to predict their total viable counts directly from FTIR spectra. The results obtained demonstrated that both approaches showed good performance in discriminating meat samples in one of the three predefined sensory classes. The PLS classification models showed performances ranging from 72.0 to 98.2% using the training dataset, and from 63.1 to 94.7% using independent testing dataset. The ANN classification model performed equally well in discriminating meat samples, with correct classification rates from 98.2 to 100% and 63.1 to 73.7% in the train and test sessions, respectively. PLS and ANN approaches were also applied to create models for the prediction of microbial counts. The performance of these was based on graphical plots and statistical indices (bias factor, accuracy factor, root mean square error). Furthermore, results demonstrated reasonably good correlation of total viable counts on meat surface with FTIR spectral data with PLS models presenting better performance indices compared to ANN.