Fabric Thickness Research Articles

Optimization of garment sewing operation standard minute value prediction using an IPSO-BP neural network

Abstract Standard minute value serves as a pivotal metric guiding the arrangement and balancing of production cycles in clothing production lines, and plays a crucial role in cost pricing and production order arrangement for clothing products. Given the complexity of the garment sewing process, ten influencing factors including fabric weight, fabric thickness, fabric density, stitching length, stitching shapes, cut pieces numbers, notch numbers, sewing technologies, sewing machine, and auxiliary accessories were identified. Upon this foundation, a standard sewing time prediction model, Improved particle swarm optimization - Back-propagation neural network (IPSO-BP), was proposed, focusing on non-quantitative factors. The IPSO-BP model was trained using actual sewing data from a women’s clothing production company. Compared to the unoptimized BP neural network, the IPSO-BP model demonstrated significant advantages in terms of convergence speed and prediction accuracy. Therefore, the IPSO-BP model proposed in this study holds promise for predicting standard sewing hours effectively.

A corrosion model for CZ cladding based on in-pile irradiation data

Corrosion resistance is the critical in-pile property for developing new Zircaloy cladding. As a new Zircaloy developed by China General Nuclear Power Corporation, CZ will be used as fuel rod cladding. This study is devoted to investigating the corrosion behavior of CZ. A two-stage Arrhenius equation is proposed to model the corrosion rate. A total of 1,510 measurement data points taken from 151 fuel rods irradiated for 1–4 cycles were used to develop the corrosion model. The burnup of these fuel rods ranges from 13.7 MWd/kgU to 55.9 MWd/kgU. The mean value and standard deviation of P−M (predicted value minus measured value) are 0.54 and 3.65 microns, respectively, and the mean value and standard deviation of P/M (predicted value divided by measured value) are 1.09 and 0.34 microns, respectively. P−M and P/M increase with the local burnup. When the burnup is greater than 40 MWd/kgU, the mean values of P−M and P/M are 6.2 and 1.20 microns, respectively; on the other hand, the prediction of the proposed model is more conservative in high burnup. In the typical case in which the average burnup reaches 57 MWd/kgU, the oxide thickness of CZ cladding is much less than 30 microns, whereas the oxide thickness of Zr-4 cladding is approximately 80 microns, and this manifests that CZ cladding possesses excellent performance in corrosion resistance.

Evaluation of thermal comfort and air permeability properties of Lyocell blended drapery fabrics

PurposeThe paper aims to provide an investigation about the effect of weft yarn type on thermal comfort and air permeability properties of Lyocell blended drapery fabrics.Design/methodology/approachThe paper evaluates the effect of weft yarn type on thermal comfort and air permeability properties of Lyocell blended drapery fabrics. Twill drapery fabrics with 18 Tex linen warp yarn where two types of weft yarns were utilized respectively with the order of “A” yarn and “B” yarn. 58 Tex Lyocell Linen blended first weft yarn (A yarn) was kept constant and the second weft yarn (B yarn) varied in different yarn structures and yarn count. Thermal comfort properties such as thermal conductivity, thermal resistivity, thermal absorptivity, fabric thickness were measured by means of Alambeta device. Correlation matrix between the thermal properties was also displayed. Air permeability results were obtained by using SDL Atlas Digital Air Permeability Tester Model M 021 A. One way analysis of variance (ANOVA) test was performed in order to investigate the effect of weft yarn type on thermal comfort and air permeability properties of Lyocell blended drapery fabrics.FindingsIn this paper, weft yarn type was found as a significant factor on some of the thermal comfort properties such as thermal conductivity, thermal resistivity, thermal absorptivity, fabric thickness and on the air permeability properties.Originality/valueThere are limited works related to evaluation of some thermal comfort and air permeability properties of Lyocell blended drapery fabrics.

Advancing Rock-Socketed Pile Design with a Unified Interface Shear Strength Framework for Soft Rocks

The shaft resistance of rock-socketed piles (RSPs) is primarily influenced by the interactions between the pile, rock and any soft interface materials. This study presents a fundamental experimental and numerical investigation to predict the shaft resistance of model RSPs in soft rocks through a comprehensive shear strength framework incorporating the major variables such as roughness and smear fabric. By calibrating the Discrete Element Method (DEM) results with the experimental outcomes, this study evaluates the load-resistance attributes of RSPs in three different soft rocks for a wide range of roughness and smear configurations. The interface roughness effect of the RSPs in terms of the friction coefficient was correlated with the ultimate shaft resistances, uniaxial compressive strength and Young’s modulus of the rock. The study then incorporated the effect of smear fabric (placement, thickness and area proportion) in the shear strength framework of clean shafts. Comprehensive review of the DEM results revealed that the socket roughness effect diminishes at a critical roughness factor (RF)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$({\ ext{RF}})$$\\end{document} of 0.4, beyond which the smear predominantly influences the shaft capacity. Following this, the effects of roughness and smear fabric were incorporated into a single equation representing the interface shear strength of RSPs, where the existence of smear results in a maximum reduction of up to 75% of the ultimate shaft resistance. The distinctive feature of this unified interface shear strength framework lies in its integration of the new smear fabric parameter and the linkage to the mechanical properties of soft rocks, which is the limitation of the earlier studies. It thereby sets a strong base for future studies aimed at advancing the pile-rock interface models.

Insights into the hexamine-assisted ZnO based-MOFs formation on PET fabric for improved self-cleaning, flame retardant, and hydrophilic properties

Organo-metallic frameworks (MOFs) have attracted much attention in the last decade due to their large pore size, large surface area, selective adsorption of small molecules, and optical or magnetic responses in the presence of guest molecules. Particularly, the use of MOFs on textile products through in situ synthesis leads to special properties in a simple method. Here, the polyester fabric was first activated by alkali hydrolysis and then the ZnO-based MOFs were synthesized to obtain a fabric with multifunctional features. FESEM images and EDX elemental analysis showed the presence of MOF particles and zinc, nitrogen, and hydrogen on the polyester surface. The weight of the treated samples increased however the water spreading time decreased due to the polar groups of the MOFs creating higher hydrophilic properties. The presence of hydroxyl and amine groups on the surface of the MOFs-treated polyester was confirmed by FTIR spectroscopy. The final fabric showed photocatalytic self-cleaning, and UV protection properties. In addition, TGA, ash percentage, and burnt length results demonstrated the flame-retardant properties. The tensile strength, thickness, and stiffness of the MOFs-treated fabrics also increased. Finally, the hexamine-assisted ZnO-based MOFs on the polyester surface improve its properties and usability.

Single-pass high-power laser-Arc hybrid welding of thick stainless steel clad plates: Microstructure and mechanical properties

In order to solve the existing problem of low efficiency of multi-layer and multi-pass welding methods for thick stainless steel clad plate (SSCP), this paper proposes high-power laser-arc hybrid welding (HP-LAHW) single-pass weld thick SSCP. The effects of wire feeding rate (WFR) on the weld's composition distribution, microstructure and mechanical properties were investigated. The weld morphology is nail-shaped, consisting of a wide arc zone (ArcZ) within the stainless steel layer and a long narrow laser zone (LaserZ), mainly in the carbon steel layer. With the increase of WFR, the dilution rate of the carbon steel layer to the ArcZ decreases first and then increases. The influence of WFR on the microstructure and mechanical properties of the weld is observed mainly in the ArcZ. When the WFR reaches 6 m/min, the composition of the microstructure within the ArcZ will change from martensite, ferrite, and a small amount of granular austenite to ferrite and a small amount of austenite, resulting in the microhardness of the ArcZ decreasing rapidly from 398 HV to 210 HV. The solidification type and microstructure transformation mechanisms within the weld were elucidated based on quantitative calculations of the chromium and nickel equivalent ratios. The microstructure of the LaserZ in all welds is predominantly martensitic due to the rapid cooling rate, which makes the tensile properties of the weld higher than the base metal (BM). The results of this study provide technical support for realizing high-efficiency and high-performance single-pass HP-LAHW of thick SSCP.

Influence of cladding tube structure on transmission characteristics of negative curvature terahertz fiber

A novel type of polarization negative curvature terahertz fiber has been designed, which has a cladding region composed of two long semi-circular cladding tubes in the horizontal direction and four long semi-elliptical cladding tubes in the oblique direction. The optimal fiber dimensions are determined to investigate the internal relationship between polarization characteristics and fiber cladding structure. The transmission performances of the negative curvature fiber are investigated by varying the thickness of fiber cladding tubes and adjusting the structural characteristics of cladding tubes in different directions. Relatively good transmission performances can be obtained with a cladding tube thickness of 80 μm. The birefringence can be stably maintained at the order of 10−4, and the confinement loss of the x-polarized mode at 2.55 THz is 0.036 dB cm−1. Moreover, the dispersion coefficient of the two polarized modes is stable between ±0.6 ps (THz·cm)−1 in the 2.3–2.6 THz band. The bent performance of the designed fiber at 2.52 THz is also discussed. The results show that the fiber has low bending sensitivity, and can still maintain excellent birefringence and low confinement loss in the bending state. For the y polarization, the birefringence can be basically stabilized at the order of 10−4 within a bending radius of 10–60 cm.

Non-destructive reverberant testing of natural fibrous samples in a diffused acoustic field environment

A hexahedron reverberation box was utilized to measure the reverberation time of samples in a diffuse acoustic field. The aim of this study is to design a mathematical model that accurately represents the correlation between frequency and absorption coefficient for analysis. The same was obtained by aligning the noise absorption coefficients (NACs) determined through an impedance tube and calculated based on the reverberation time. The analysis indicated that jute fiber exhibits superior performance compared to other natural fibers, such as rock and glass wool, in the attenuation of sound frequencies above 2200 Hz. The results motivated further investigation over jute nonwoven fabric to conduct layer analysis (1–7) and to examine the impact of thickness. Thereby, a dataset was compiled consisting of 11 fibrous samples (nine natural and two commercial fibers) and jute nonwoven fabric. The empirical model was developed regardless of the type of fiber or thickness of the nonwoven fabric, and it was successfully validated for three additional fibers (banana, pineapple, and ramie). The predictive model exhibited a high level of accuracy in estimating the NAC, displaying a strong similarity to that of impedance tube measurements. The achieved mean absolute error ranges for the predictions are between 0.02 and 0.03 only. The main discovery of this study revolves around the recognition of frequency of sound as a crucial variable and its application in predicting the NAC for the samples.

Investigation of seaming performance of dyed and finished polyester/viscose fabrics

Seaming performance of a fabric is an important property because seams on a garment play a major role in determining the quality of the garment. Seam efficiency is calculated by the ratio of seam strength to fabric tensile strength. Seam strength and fabric strength are affected by the fabric-related properties and the stitch-related parameters. Therefore, as well as the effects of seaming parameters, dyeing, finishing, and laundering processes that the fabric is exposed to before the seaming processes have an effect on the seam efficiency. Polyester/viscose blended fabrics, which are frequently preferred in textiles, are used in this study, and the effects of bleaching, dyeing, finishing, laundering, and seaming processes on seam strength and seam efficiency, fabric tensile strength and fabric tensile elongation, air permeability, and thickness properties of these fabrics are examined. After the processes, there will be an increase or decrease in the properties depending on which process is examined in detail. The results are evaluated statistically. According to the results obtained, it has been observed that the mentioned processes are effective on seam efficiency. It can be said that seam strength and seam efficiency values are affected by the processes in different ways, and also that seam parameters are effective on seam strength values. Different effects of fabric direction on tensile strength and seam strength values were also observed. As explained in the study, these processes affect the other features differently.

Electromagnetic shielding effectiveness of needle-punched composite nonwoven fabrics with stainless steel fibres

In the study, electromagnetic shielding efficiency (EMSE) absorption and reflectivity properties of fabric produced from staple stainless-steel fibres and recycled staple polyester fibres by carding and needling technologies were investigated. The bi-component core/sheath polyester fibres at a fixed ratio of 20% in producing all nonwoven fabrics were used. The staple stainless-steel fibres and recycled staple polyester fibres were blended at 13 different ratios such as 1%, 2.5%, 5%, 7.5%, 10%, 12.5%, 15%, 17.5%, 20%, 22.5%, 25%, 27.5%, 30%. The fibre webs were formed at wool type carding machine and then the folded webs were bonded mechanically with needle punching machines. Half of the produced nonwoven composite fabrics were bonded by thermal bonding technology with oven and calender machines. As the conductive fibres were costly, the study aimed to obtain optimum shielding effectiveness with the usage of minimum conductive fibres. Electromagnetic shielding properties, absorption and reflection characteristics of needle-punched nonwoven fabrics with calendered or un-calendered were performed by coaxial transmission line method according to ASTM-D4935-10 in the frequency range of 15 MHz to 3000 MHz. It is a known fact that electromagnetic shielding effectiveness increases with the increase in the amount of conductive fibre. It was found that nonwoven fabric produced with a usage of 17.5% stainless steel fibre has at least 90% electromagnetic shielding percentage in general use with 15 dB at a frequency of 1800 MHz. Increased stainless steel fibre content in nonwoven fabrics resulted in decreased nonwoven fabric thickness and tensile strength. Such a nonwoven composite material with electromagnetic shielding property could be used for construction and building applications.

Achieving highly accurate cavity thickness measurements in fabric compaction

The compaction of a fabric reinforcement in a Universal Testing Machine (UTM) allows to determine the achievable fiber volume fraction across a wide range of pressures, a valuable information for composite manufacturing. As seen in the first international compressibility benchmark, inaccuracies in the fabric stack thickness measurement, the approach to compliance correction and the non-parallelism between compaction plates resulted in highly inaccurate compression curves. In this paper, the different variability sources affecting indirect thickness methods, based on the machine displacement, and direct methods with laser sensors are presented and its impact on the accuracy is estimated. In conclusion, both thickness measurement methods produced similar results; however, the thickness measured by direct methods experienced more variability due to minor changes in the rig’s displacement or the orientations between plates, combined with other sources of variability such as external interferences or vibrations from the compaction plate which led to variations in measurement precision throughout the tests.

Artificial neural network and multiple linear regression modeling for predicting thermal transmittance of plain-woven cotton fabric

The present research compares a machine learning model with a statistical model, with specific emphasis on artificial neural networks and multiple linear regression models. The aim of this study is to forecast the thermal transmittance of a plain-woven cotton fabric using input data such as thread density measured in ends per inch, picks per inch, and fabric thickness. The artificial neural network is built using a network with feed-forward backpropagation, and the MATLAB software’s training function trainlm is used to modify its weight and basic values based on Levenberg–Marquardt optimization techniques. The sigmoid transfer function is used to set the layer output and measure network performance in terms of the root mean squared error, mean absolute error percentage, and coefficient of determination which were determined. For the artificial neural network prediction model, the root mean squared error and mean absolute error percentage were 1.05 and 3.132%, respectively, while the coefficient of determination was 0.9307. In contrast, the multiple linear regression prediction model had root mean squared error and mean absolute error percentage values of 2.98 and 8.97%, respectively, along with a coefficient of determination of 0.4727. The results reveal that the artificial neural network model outperforms the multiple linear regression model, showing superior accuracy and robustness in capturing the intricate interactions between important fabric parameters (ends per inch, picks per inch, and thickness) and thermal transmittance values. This research emphasizes the efficiency of artificial neural network modeling as a superior tool for forecasting thermal transmittance in textile applications rather than employing the time-consuming trial-and-error process for delivering significant insights for material engineering and energy-efficient design.

Predicting the thermal protective performance of flame-retardant fabric based on machine learning

PurposeThe purpose of this study is to predict the thermal protective performance (TPP) of flame-retardant fabric more economically using machine learning and analyze the factors affecting the TPP using model visualization.Design/methodology/approachA total of 13 machine learning models were trained by collecting 414 datasets of typical flame-retardant fabric from current literature. The optimal performance model was used for feature importance ranking and correlation variable analysis through model visualization.FindingsFive models with better performance were screened, all of which showed R2 greater than 0.96 and root mean squared error less than 3.0. Heat map results revealed that the TPP of fabrics differed significantly under different types of thermal exposure. The effect of fabric weight was more apparent in the flame or low thermal radiation environment. The increase in fabric weight, fabric thickness, air gap width and relative humidity of the air gap improved the TPP of the fabric.Practical implicationsThe findings suggested that the visual analysis method of machine learning can intuitively understand the change trend and range of second-degree burn time under the influence of multiple variables. The established models can be used to predict the TPP of fabrics, providing a reference for researchers to carry out relevant research.Originality/valueThe findings of this study contribute directional insights for optimizing the structure of thermal protective clothing, and introduce innovative perspectives and methodologies for advancing heat transfer modeling in thermal protective clothing.

A novel collision model for inextensible textiles and its experimental validation

In this work, we introduce a collision model specifically tailored for the simulation of inextensible textiles. The model considers friction, contacts, and inextensibility constraints all at the same time without any decoupling. Self-collisions are modeled in a natural way that allows considering the thickness of cloth without introducing unwanted oscillations. The discretization of the equations of motion leads naturally to a sequence of quadratic problems with inequality and equality constraints. In order to solve these problems efficiently, we develop a novel active-set algorithm that takes into account past active constraints to accelerate the resolution of unresolved contacts. We put to a test the developed collision procedure with diverse scenarios involving static and dynamic friction, sharp objects, and complex-topology folding sequences. Finally, we perform an experimental validation of the collision model by comparing simulations with recordings of real textiles as given by a Motion Capture System. The results are very accurate, having errors around 1 cm for DIN A2 textiles (42 x 59.4 cm) even in difficult scenarios involving fast and strong hits with a rigid object.

Microscopy of Woven and Nonwoven Face Covering Materials: Implications for Particle Filtration.

A suite of natural, synthetic, and mixed synthetic-natural woven fabrics, along with nonwoven filtration layers from a surgical mask and an N95 respirator, was examined using visible light microscopy, scanning electron microscopy, and micro-X-ray computed tomography (µXCT) to determine the fiber diameter distribution, fabric thickness, and the volume of solid space of the fabrics. Nonwoven materials exhibit a positively skewed distribution of fiber diameters with a mean value of ≈3 μm, whereas woven fabrics exhibit a normal distribution of diameters with mean values roughly five times larger (>15 μm). The mean thickness of the N95 filtration material is 1093 μm and is greater than that of the woven fabrics that span from 420 to 650 μm. A new procedure for measuring the thickness of flannel fabrics is proposed that accounts for raised fibers. µXCT allowed for a quantitative nondestructive approach to measure fabric porosity as well as the surface area/volume. Cotton flannel showed the largest mean isotropy of any fabric, though fiber order within the weave is poorly represented in the surface electron images. Surface fabric isotropy and surface area/volume ratios are proposed as useful microstructural quantities to consider for future particle filtration modeling efforts of woven materials.

Unsaturated out-of-plane permeability measured with three-dimensional flow monitoring by etched optical fiber sensors

In our work, the three-dimensional flow in stacks of biaxial E-glass fiber non-crimp fabrics (NCF) and unidirectional carbon fiber layers was monitored by the etched optical fiber sensors embedded inside the fabric stack. The influence of embedding sensors with a diameter of 200 µm on the flow was experimentally illustrated and discussed. The out-of-plane permeability of the two fabric materials was estimated with a radial infusion model using the in-plane permeability values as the prior knowledge. The assumed radius of the semi-ellipsoidal flow was derived and showed a good agreement with the results obtained by the best fit of the exact solution, which provides an approximate solution that simplifies the calculation process. The asymptotic form and the modified semi-ellipsoidal inlet radius method for the case of a non-negligible radius of injection tube compared to the fabric sample thickness were verified. The uncertainties related to the unknown flow pattern near the inlet were also demonstrated. The presented results facilitate the further development and standardization of out-of-plane permeability measurement methods with embedded sensors.

Experimental investigation of mechanical and comfort properties of banana/cotton blended woven fabrics

Purpose Through this study, four different types of woven fabric structures were created by using cotton/banana blends with a 70:30 ratio by varying the weaving specifications. This study aims to investigate the comfort and mechanical properties of these woven materials. Design/methodology/approach Taguchi L16 experimental design (5 factors and 4 levels) with response surface methodology tool was used to optimize mechanical and comfort characteristics. The yarn samples used in this study are cotton/banana with a blend ratio of 70:30. Fabric type (A), grams per square metre (GSM; B), yarn count (C), fabric thickness (D) and cloth cover factor (E) are the chosen process characteristics. Findings The highest tensile strength and tearing strength of the cotton/banana blended fabric samples were obtained as 326.3 N and 90.3 k.gf/cm, respectively. Similarly, the highest thermal conductivity and overall moisture management capacity values were found to be 0.6628 and 3.06 W/mK X10−4, respectively. The optimized process parameters for obtaining maximum mechanical properties were using canvas fabric structure, 182 GSM, 36s Ne yarn count, 0.48 mm fabric thickness and 23.5 cloth cover factor. Similarly, the optimized process parameters for obtaining maximum comfort properties were achieved using a twill fabric structure, 182 GSM, 32s Ne yarn count, 0.4 mm fabric thickness and 23 cloth cover factor. Originality/value In contrast to synthetic fabrics, banana fibre and its blended materials are significant ecological solutions for apparel and functional clothing. Products made from banana fibre are a sustainable and green alternative to conventional fabrics. Banana fibre obtained from the pseudostem of the plant has an appearance similar to ramie and bamboo fibres. Numerous studies showed that banana fibre could absorb significant moisture and be spun into yarn through ring and rotor spinning technology. On the other hand, this fibre can be easily combined with cotton, jute, wool and synthetic fibre. The present utilization of pseudostem of banana plant fibre is very minimal. This type of research improves the usability of bananas their blended fabrics as apparel and functional wear.

A Comparative Study of Inner Neck Scarf Quality from Different Price Range among the Malaysian Consumers

The inner neck scarf is used to grip the forehead until covering the end of the neck. Due to the many types of it in the market and the need for more information on better quality, customers buy it without knowing whether it is worth the performance offered. This study evaluates comfort properties in terms of the quality of three brands: dUCK Scarf, Naelofar Hijab, and unbranded. Comfort properties such as breathability and absorbency were tested. Its physical properties were also tested, including fabric weight, thickness, and stitch density. Naelofar Hijab's inner neck gives better breathability and absorbency testing results.

Геометрическая модель прогнозирования свойств систем с интервальными параметрами

We consider interval geometric modeling of complex multi parametric systems having a set of parameters of different character. Some of the parameters may have interval indefiniteness. System information basis is incomplete one. The processed information depends on continuous, discrete and conventional data. Geometric model has a form of matrix and each element of it corresponds to some state of the system. Each state is described by interval function of continuous input and output parameters. The set of interval functions generates some discrete set of multidimensional surfaces in discrete space. We use this approach and our modeling algorithm to find predictive model of drape coefficient. The algorithm is based on linear approximation of numerical factors in factor spaces. Interval functions make it possible for us to vary some numerical factors within the given intervals. As an example, the interval model of fabric drape coefficient is found. Fabric thickness and closeness of texture are considered as input parameters.

EFFECT OF COMMERCIAL WATER REPELLENT AGENTS ON FUNCTIONAL PROPERTIES OF POLYESTER WOVEN FABRIC USED FOR WASHABLE MEDICAL MASKS

Water repellent fabrics are always used for washable medical mask production to keep them from the bacterial liquid during use. Choosing the right water repellent which is efficient and suitable for fabric is important in washable medical mask production. The water-resistant treated fabric needs to keep its water repellent for many washed cycles. Moreover, their physico-mechanical properties such as air permeability and thickness must be less changed. In this study, four commercial water-resistant chemicals (Ruco–Coat BC 7068, TP – Phob FC 2904, Phobotex RHP Hydrophobic Agent, Ruco-guard AFB60) were used to treat the 100% polyester woven fabric to examine the influence of the type and concentration of the waterresistant chemicals on water repellent capability of the polyester woven fabric. The fabric thickness and the air permeability of the untreated and treated fabric were investigated. The SEM, FE-SEM analysis, and the FTIR spectra were used to find the differences between the initial and treated fabric. The results showed that the water-resistant type influenced the water repellent capability of fabrics and their duration. Among four investigated water resistance, the TP – Phob FC 2904 presented the best water resistance for treatment of the 100% polyester woven fabric, and its concentration of 50 g/l has maintained 85 % fabric water repellent capability after 25 washed cycles.