Optimization of the needle punching process for the nonwoven fabrics with multiple quality characteristics by grey-based taguchi method

This study is intended for finding out the optimal processing parameters for needle punching nonwoven fabrics in order to work out its maximal strength. Taguchi method together with grey relational analysis is employed to resolve the problem as regards multiple-quality optimization, and further discover the optimal combination of processing parameters for needle punching nonwoven fabrics. Firstly, orthogonal array L₁₈(2¹×3⁷) is used to deal with the processing parameters that may exert influence over the manufacturing of needle punching nonwoven fabrics. Then grey relational analysis is applied to resolve the deficiency of Taguchi method that focus on single quality characteristic. Next, the response table of grey relational analysis is used to obtain the optimal combination of processing parameters for multiple quality characteristics. In the current experiment quality characteristic refers to the tensile strength and tear strength of the nonwoven fabrics. Additionally, signal-to-noise ratio (SN ratio) calculation and analysis of variance (ANOVA) can be adopted to explore the experimental results. Through ANOVA, the significant factors that exert comparatively significant influence over the quality characteristic of the needle punching nonwoven fabrics, that is, the control factors are determined so that the quality characteristic of the needle punching nonwoven fabrics can be effectively controlled. Finally, confirmation experiment is conducted within 95 % confidence interval to verify the experimental reliability and reproducibility.     

Processing parameters optimization of multiple quality characteristics of open-end rotor spinning process for Bamboo charcoal and CVC blended fibers

In the field of yarn spinning engineering, the importance of the processing parameters taken depends directly on the quality characteristics of the yarn. This study aimed to find the optimal processing parameters for an open-end rotor spinning frame at work to identify its multiple quality characteristics for yarn. In this study, Bamboo charcoal and cotton 70 %/polyester 30 % (CVC) blended fibers were adopted as the materials, and the open-end rotor spinning frame was used to spin the yarn. In order to identify optimal conditions of an open-end rotor spinning frame, the Taguchi experimental method was applied to design open-end rotor spinning experiments, and the L₉ orthogonal array was chosen in accordance with nine sets of experiments and contained four control factors and three levels. Furthermore, a response surface methodology (RSM) was used to obtain the models of significant processing parameters for the strength, unevenness, I.P.I, and hairiness. Based on experiments designed to obtain an open-end rotor spun yarn Ne 30, the strength, unevenness, imperfection indicator/km (I.P.I) and hairiness were then chosen as the quality characteristics. In addition, grey relational analysis integrated the optimal processing parameter of multiple quality characteristics, and a confirmation experiment was performed. In conclusion, the optimal processing parameters under steady spinning conditions were a rotor speed of 88000 rpm, a feed speed of 0.392 m/min, and a winding speed of 39.466 m/min.     

Analysis and construction of a quality prediction system for needle-punched non-woven fabrics

In this study, polyester and polypropylene staple fibers were selected as the raw material, and then processed through roller-carder, cross-lapper and needle-punching machine to produce needle-punched non-woven fabrics. First, the experiment was planned using the Taguchi method to select processing parameters that affect the quality of the needle-punched non-woven fabric to act as the control factors for this experiment. The quality characteristics were the longitudinal and transverse tensile strength of the non-woven fabric as well as longitudinal and transverse tear strength. The L₁₈ (2¹×3⁷) orthogonal array was selected for the experiment as it offered an improvement on the traditional method that wastes a lot of time, effort and cost. By using the analysis of variance (ANOVA) technique at the same time, the effect of significant factors on the production process of needle-punched non-woven fabrics could be determined. Finally, the processing parameters were set as the input parameters of a back-propagation neural network (BPNN). The BPNN consists of an input layer, a hidden layer and an output layer where the longitudinal/transverse tensile and tear strength of the non-woven fabric were set as the output parameters. This was used to construct a quality prediction system for needle-punched non-woven fabrics. The experimental results indicated that the prediction system implemented in this study provided accurate predictions.     

Combining Taguchi Method with fuzzy inference on process optimization for fiber manufacturing

Lack of natural textile resources, the present textile industry in Taiwan usually uses pre-oriented yarn (POY), kind of artificial fibers, to make yarns. The POY is wound from continuous spinning of esterification and superposition of plastic pure terephthalic acid (PTA) and ethylene glycol (EG). According to yarn assessment indicators, yarn breakage of POY is crucial. And the broken filament and toughness are the most two important indicators causing yarn breakage during quality measurement. This study applies Taguchi Method to jointly consolidate broken filament degradation rate and toughness elongation percentage to establish a proper orthogonal array. The experimental control factors includes knotting device type, winding tension (CN), oil rate (%), and knotting pressure (kg/cm²), and a L ₁₈(2¹×3⁷) orthogonal array is established. The key parameter design of control factors can be found by Taguchi experiment. The fuzzy inference is combined with Taguchi multiple quality characteristics to construct the process parameter module to effectively increase product yield.     

Effect of processing conditions on warpage of film insert molded parts

In order to investigate effects of injection molding conditions on viscoelastic behavior and thermal deformation of film insert molded (FIM) parts, injection molding was performed with various conditions such as injection speed, melt temperature, and packing time. It was shown that variation of the warpage was decreased monotonically with increasing injection speed and exhibited a bell-shaped curve as a function of melt temperature. Warpage variation was not affected by the packing time significantly and the proportional relationship between warpage of the film insert molded part and shrinkage of the injection molded part without film was observed. The FIM specimens produced with unannealed films showed the warpage reversal phenomenon (WRP) during annealing and the magnitude of reversed warpage was affected significantly by the injection parameters and the extent of thermal shrinkage of the unannealed film. Warpage of the FIM specimen was predicted by three dimensional numerical flow and stress analyses and the predicted values showed a good agreement with the experimental results.     

Prediction system of open-end rotor spinning process based on LM-neural network for bamboo charcoal fibers

In line with the environmental protection trends of the 21st century, bamboo charcoal fiber is invented to meet the requirements of the fields of science and technology. Its special functionalities, namely, antistatic, moisture adsorptive, perspiring, antibacterial, deodorizing, anti-radiation, and far infrared properties make it extremely suitable for applications in medicine, sports, and recreational fields, as well as an important breakthrough for environmentally-friendly textile materials. To achieve rapid manufacturing, this study processes bamboo charcoal fibers by open-end (OE) rotor spinning. The Taguchi orthogonal array is applied to the design of this experiment, and the significant factors of fibers quality are obtained through ANOVA in order to facilitate the follow-up processes of quality control. The process prediction system is built based on the test data, and is combined with the back-propagation neural network and the Levenberg-Marquardt (LM) algorithm in order to establish an OE rotor spinning process prediction system. The rotor speed, feed speed, and winding speed are set as the network input parameters, while the yarn strength, the hairiness, the unevenness, and the imperfections indicator (I.P.I.) are the output parameters. Through network learning and training, this system reports a prediction error below 5 %, proving that this prediction system has excellent predictability.     

The study of processing parameters on impact behavior of high performance polyethylene fiber cross-ply composites by taguchi method

In this study, the effect of processing parameters such as temperature, pressure, time of compaction process and areal density on high-velocity impact behaviour of high performance polyethylene fibre cross-ply composites were investigated by Taguchi method. Samples were made through high temperature and pressure compacting process and morphology and interlayer adhesive of samples were investigated by scanning electron microscopy “SEM and T-peel test, repectively. Taguchi method was used to plan a minimum number of experiments. Statistical analysis, analysis of variance (ANOVA), was also employed to determine the relationship between experimental conditions and yield levels. ANOVA was applied to calculate sum of square, variance, ratio of factors variance to error variance and contribution percentage of each factors on response. A hemispherical tip type projectile was used for high velocity impact tests and the depth of trauma as the response factor was measured after impacting test. Results showed that when the temperature, pressure, and time of compacting process were 125 °C, 3 MPa, and 30 min for the composite sample with 7.4 kg/m² areal density, the trauma depth was decreased to its lowest value.     

9个燕麦品种在齐齐哈尔地区的农艺性状及饲草品质评价

为筛选出适宜在齐齐哈尔地区推广种植的燕麦品种，用基于熵权的灰色关联方法对9个燕麦品种的农艺性状和饲草品质进行综合评价。结果发现，9个品种中，梦龙株高达到120 cm以上，与吉利和摩登差异不显著，但显著高于其他品种(P<0.05);悍马鲜草产量最高(49 395 kg·hm^-2),龙牧12号干草产量最高(13 837 kg·hm^-2)。9个燕麦品种的可溶性碳水化合物(WSC)、粗蛋白(CP)、粗脂肪(EE)、酸性洗涤纤维(ADF)、中性洗涤纤维(NDF)、粗灰分(ASH)、木质素(ADL)含量和相对牧草质量(RFQ)分别为10.52%～12.55%、6.58%～9.88%、1.96%～2.83%、32.67%～39.52%、59.47%～68.34%、6.99%～10.40%、3.50%～4.47%、85.00～110.67。梦龙、龙牧12号和吉利的CP含量显著低于其他品种(P<0.05);泰克的ASH含量显著高于其他品种(P<0.05);龙牧12号的ADF和NDF含量均显著高于其他品种(P<0.05)。应用灰色关联度法...     

Injection molding of vertebral fixed cage implant

A vertebral cage is a hollow medical device which is used in spine surgery. By implanting the cage into the spine column, it is possible to restore disc and relieve pressure on the nerve roots. Most cages have been made of titanium alloys but they detract the biocompatibility. Currently PEEK (polyether ether ketone) is applied to various implants because it has good properties like heat resistance, chemical resistance, strength, and especially biocompatibility. A new shape of vertebral cage is designed and injection molding of PEEK is considered for production. Before injection molding of the cage, it is needed to evaluate process conditions and properties of the final product. Variables affecting the shrinkage of the cage are considered, e.g., injection time, packing pressure, mold temperature, and melt temperature. By using the numerical simulation program, MOLDFLOW, several cases are studied. Data files obtained by MOLDFLOW analysis are used for stress analysis with ABAQUS, and shrinkage and residual stress fields are predicted. With these results, optimum process conditions are determined.     

苎麻纤维性能与成纱质量的人工神经网络分析

本文分别采用三种方法-BP神经网络、灰色关联分析结合BP神经网络、主成分结合BP神经网络根据苎麻纤维的性能建立了成纱性能的预测模型。采用灰色关联分析和主成分分析可以减少BP神经网络的输入节点数,提高预测结果的精度和稳定性。与单纯的BP神经网络的预测结果相比,灰色分析结合BP神经网络和主成分分析结合BP神经网络的预测结果更准确,在对成纱性能进行预测时,预测值与实测值之间的平均相对误差均明显下降。     

A study of physical modification on grey cotton by laser irradiation

The surface morphology of the CO₂ laser treated grey cotton fabrics was studied which showed a characteristics sponge-like structure on cotton fibres after treating with CO₂ laser irradiation. The laser treatment parameters ranging from 100 to 150 pixel time and 40 to 70 dot per inch (dpi) were irradiated on the grey cotton fabrics directly and the degree of physical modifications, such as surface morphology, wettability and fabric strength, were changed accordingly with various laser treatment parameters. The surface morphology, wettability and tensile strength of cotton fibre treating with laser were evaluated using different instruments, such as Scanning Electron Microscope (SEM), contact angle meter and tensile strength machine. In spite of creating a sponge-like structure on fibre surface after treating with laser, the wettability of the samples was highly improved but the tensile strength was decreased.     

Multiple response optimization of rotor yarn for strength,unevenness, hairiness and imperfections

In this study, a multiple response optimization model based on response surface methodology was developed to determine the best rotor speed and yarn twist level for optimum rotor yarn strength and unevenness, and minimum yarn hairiness and imperfections. Cotton yarn of 30 tex, was produced on rotor spinning machine with different twist levels (i.e. 500, 550, 600 and 700 tpm) at different rotor speeds (i.e. 70000, 80000, 90000 and 100000 rpm). Yarn quality characteristics were determined for all the experiments. Based on the results, multiple response optimization model was developed using response surface regression on MINITAB® 16 statistical tool. Optimization results indicate that with the quality of raw material selected for this study, top 50 % quality level, according to USTER® yarn quality benchmarks, can be achieved with 100 % desirability satisfaction for all the selected yarn quality parameters at rotor speed of 77,800 rpm and yarn twist of 700 twists per meter.     

沼液追施量对小麦蛋白组分、面团流变学和淀粉糊化特性的影响

为给沼液在小麦优质高产栽培中的科学施用提供理论依据,在大田高产条件下研究了沼液追施量对小麦主要品质性状的影响。结果表明,适宜的沼液追施量能有效地提高小麦淀粉黏度参数和蛋白质及其组分含量,改善面团流变学特性。淀粉糊化特性、蛋白质组分含量和粉质参数均以60~120 kg·hm^-2沼液氮追施量最为适宜,而拉伸参数的沼液氮优化水平相对较高（180 kg·hm^-2）,沼液追施量不足或过多均不利于籽粒品质参数的改善。因此,在基施酰氨态氮120 kg·hm^-2的基础上,追施120 kg·hm^-2沼液氮最为适宜,品质指标协调,营养价值高,品质综合性状好。     

安徽麦区软质小麦籽粒品质和终端制品品质评价

为明确安徽大田生产环境下软质小麦籽粒和终端产品品质表现,评价优质软麦品种的加工适用性,本研究选取该区当前推广种植的24个软质小麦品种,对其籽粒和面粉的主要品质性状及其制品南方馒头和曲奇饼干的品质进行差异性、相关性分析,并以美国软白麦近五年的主要品质性状平均值为理想指标进行灰色关联度比较。结果表明,供试材料的硬度、面粉色泽b^*、湿面筋含量、面团形成时间、稳定时间等籽粒品质性状变异系数较大,而容重、面粉L^*值和吸水率变异系数较小。南方馒头品质性状中,白度差异最小,比容差异最大;曲奇饼干品质性状中,感官评分变异系数较大,饼干直径均值和变异系数都较小。蛋白质含量、湿面筋含量、稳定时间均符合弱筋标准(GB/T 17320-2013)的样品数为0。相关分析表明,容重、降落值、面粉L^*、b^*、白度与大部分性状间相关性不显著;籽粒硬度与水SRC和乳酸SRC均呈显著正相关,与湿面筋含量和面粉a^*值均呈显著负相关。蛋白质含量与面粉a^*值等7个指标均呈显著正相关,与面粉b     

Processing parameters optimization of draw frame for rotor spun yarn strength using gene expression programming (GEP)

Breaking strength is one of the most important mechanical property of a yarn as it is the main parameter for quality control. This property depends on many different factors namely, raw material factors, process variables and machine parameters. Since, there is a high degree of interaction between yarn properties and influencing factors therefore, optimal processing conditions can not be determined easily. This article proposes prediction approach for the determination of the breaking strength of the yarn using gene expression programming (GEP) and optimization technique using MATLAB software. A nonlinear mathematical function was derived on the basis of draw frame variables that were distance between back and middle rolls, delivery speed and break draft by GEP. Afterward, optimal conditions were found in such a way that breaking strength to be maximized. Study showed that, optimal processing parameters including distance between back and middle rolls, break draft and delivery speed were respectively, 10.70 mm, 1.90 and 541.51 m/min (687.95 or 721.32 based on the optimization procedure).     

High performance fibers production process using Taguchi experimental design

In this research, results of an experimental interaction effect of operating parameters on tensile strength carbon fibers from a commercial PAN-based precursor are investigated. Ten parameters at two and four levels (L₃₂=2¹×4⁹) were investigated: stabilization temperature at first stage (STFIS), stabilization duration time at first stage (SDTFIS), stabilization temperature at second stage (STSS), stabilization duration time at second stage (SDTSS), stabilization temperature at third stage (STTS), stabilization duration time at third stage (SDTTS), stabilization temperature at fourth stage (STFOS), stabilization duration time at fourth stage (SDTFOS), carbonization temperature (CT), and carbonization duration time (CDT). In this study, Taguchi method was used initially to plan a minimum number of experiments. Statistical analysis, analysis of variance (ANOVA), was also employed to determine the relationship between experimental conditions and yield levels. ANOVA was applied to calculate sum of square, variance, ratio of factor variance to error variance and contribution percentage of each factor on response. The results show that increasing all of parameters improves tensile strength performance. The optimum levels of influential factors, determined for tensile strength are STFIS 200 °C, SDTFIS 120 min, STSS 225 °C, SDTSS 120 min, STTS 240 °C, SDTTS 120 min, STFOS 260 °C, SDTFOS 60 min, CT 1400 °C and CDT 10 min. The results showed that CT and ODTFIS are the most and the less effective factors on response, respectively.     

基于图像处理技术的夏玉米群体长势监测研究

试验采用均匀设计,在大田条件下固定数码相机高度垂直拍摄夏玉米拔节期和大喇叭口期的群体图像,利用图像处理技术获取玉米地面覆盖度,建立覆盖度与人工测得的叶面积指数(LAI)和干物质积累(DMA)的回归关系模型,并对该模型的适用性进行统计检验。结果表明：地面覆盖度与LAI和DMA间存在极显著正相关关系,相关系数分别达到了0.946和0.935,在叶面积估算模型中引入密度因素后模型的精确性得到了进一步改善,表明利用图像处理技术估测夏玉米群体长势具有很好的可行性。     

Extrusion processing characteristics of quinoa (Chenopodium quinoa Willd.) var. Cherry Vanilla

Extrusion processing characteristics of Cherry Vanilla quinoa flour (Chenopodium quinoa Willd) were investigated using a three factor response surface design to assess the impact of feed moisture, temperature, and screw speed on the physicochemical properties of quinoa extrudates. Specific mechanical energy (SME) required to extrude this quinoa variety was higher (250–500 kJ/kg) than previously reported for quinoa. The following characteristics of the extrudates were observed: expansion ratio (1.17–1.55 g/cm³), unit density (0.45–1.02 g/cm³), water absorption index (WAI) (2.33–3.05 g/g), and water solubility index (WSI) (14.5–15.87%). This quinoa flour had relatively low direct expansion compared to cereal grains such as corn or wheat, suggesting that it is not well suited for the making of direct expanded products. The study further suggests that there is a need to understand the processing characteristics of new quinoa varieties for cultivation. Understanding extrusion and other quality traits in advance will help to select the appropriate varieties that would allow food processors to meet consumer needs.     

Prediction of mechanical properties of aging B.mori silk fabric based on grey neural network model

This paper presents a grey neural network model for the prediction of mechanical properties of aging B.mori silk fabric. In the experiment, we obtained outdoor natural aging breaking strength of B.mori silk fabric from 8 samples. Then, a grey neural network GNNM (1,1) model is proposed by the means of combining GM (1,1) model with BP artificial neural network to predict mechanical properties of B.mori silk fabric. At the same time, this paper analyzed and compared the GM (1,1) model and GNNM (1,1) model by using prediction error such as the relative percentage error (RPE) and the root mean square error (RMSE). The experimental results show that the RMSE of GNNM (1,1) model is 0.0284 well below 6.1786, which is the RMSE of GM (1,1) model. It indicates the GNNM (1,1) model were better than the normal grey GM (1,1) model, when taken the prediction error as evaluation parameter.     

Mechanical Properties of Bread Crumb Prepared from Flours of Different Dough Strength

Bread quality depends in part on the textural properties of the crumb; softness and strength being two important textural attributes. This study examined differences between instrumentally-measured textural properties of the crumb of bread made from two flours; one possessing extra strong dough mixing characteristics and a second of moderate strength (red spring wheat). Bread crumb specimens, notched and un-notched, were subjected to tensile loading and the crumb's initial (elastic) modulus, stress at failure (crumb strength) and tear resistance were determined. The same mechanical parameters were determined on bread crumb that had been compressed approximately five-fold in order to destroy crumb structure. For un-notched specimens, stiffness and strength were of the order of 11 and 1 kN/m², respectively, whereas after compression they were 230 and 10 kN/m², respectively. For CWRS breadcrumb, toughness increased from 4·1 J/m²to 12·3 J/m²following crumb compression. Bread crumb made from a flour possessing extra strong dough properties was stronger than bread crumb made from a more conventional red spring wheat flour, and there was an indication that extra strong flour bread crumb specimens were stiffer. Compression of the bread crumb lessened the difference between the mechanical properties of the two bread types, particularly for strength and tear resistance. The results indicated that bread crumb structure plays a predominant role in the textural properties of bread crumb.