Susceptibility of strain-hardening cementitious composite to curing conditions as a retrofitting material for RC beams

Strain-hardening cement-based composites (SHCC) have recently been developed as repair materials for the improvement of crack control and strength of flexural members. This work focuses on strengthening and flexural enhancement using SHCC layer in tensile regions of flexural members under three different curing conditions. The curing conditions simulate the effect of different environmental conditions prevailing in the central and coastal regions of the Arabian Peninsula on the properties of SHCC as a retrofitting material. In this investigation, beams with SHCC layer were compared to control beams. The beams with SHCC layer of 50-mm thickness were cast. The results revealed that the flexural behavior and the load-carrying capacity of the normal concrete beam specimens under hot and dry environmental conditions were significantly reduced, lowering the ductility of the section. However, compressive strength is comparatively unaffected. Similarly, the hot curing conditions have also led to a notable reduction in the loading capacity of the beam with SHCC layer with a slight effect on its stiffness. On the other hand, steam-curing conditions have shown improvement in load-carrying capacity and a reduction in section ductility of the beam with SHCC layer. It was found that the structural unit retrofitted with SHCC layer was a curing-regime dependent as the tensile and strain-hardening properties of SHCC are highly sensitive to the alteration in the cement hydration process. A normal curing regime was found effective and satisfying the practical, cost, and performance requirements. Accordingly, a normal curing regime could be implemented to retrofit reinforced concrete (RC) beams with SHCC layers as recommended in the study.

Intelligent manufacturing of color blended yarn: Color matching algorithm and manufacturing process through computer numerically controlled ring spinning system

Yarn-dyed textiles complement digital printing textiles, which hold promise for high production and environmentally friendly energy efficiencies. However, the complicated structures of color-blended yarns lead to unpredictable colors in textile products and become a roadblock to developing nonpollution textile products. In the present work, we propose a framework of intelligent manufacturing of color blended yarn by combining the color prediction algorithm with a self-developed computer numerically controlled (CNC) ring spinning system. The S-N model is used for the prediction of the color blending effect of the ring-spun yarn. The optimized blending ratios of ring-spun yarn are obtained based on the proposed linear model of parameter W. Subsequently, the CNC ring-spinning frame is used to manufacture color-blended yarns, which can configure the constituent fibers in such a way that different sections of yarn exhibit different colors.

Investigation of interactions between fabric performance, sewing process parameters and seam pucker of shirt fabric

Seam pucker is a common problem in sewing. It not only affects the appearance of product, but also affects product performance. The purpose of this study is to quantify the complex dynamic interactions between fabric performance, sewing process parameters and seam pucker. In order to solve the problem of shirt seam pucker, this study selected four kinds of shirt fabrics, three kinds of polyester sewing threads, three kinds of stitch density and four kinds of seam types for experiments. Through unitary regression analysis, the subjective and objective evaluation results are consistent. Further analysis the results of objective experiment revealed that fabric performances, seams type, sewing thread and stitch densities all have impact on seam pucker. Meanwhile also find out the sewing process parameters for the four fabrics when the seam shrinkage’s were smallest, so it’s helpful for the apparel enterprises to improve seam quality. Multiple linear regression analysis of experimental results show that fabric performances has the greatest influence on seam pucker, thickness, weight and warp density of fabric properties significantly affect seam pucker. And as the breaking elongation of sewing thread increases, seam pucker also increases. Stitch densities and seam type has the least affected on seam pucker, they affect the seam pucker by changing the extension of stitch and thickness of fabric at the seam, respectively. Seam type has greater impact on fabrics that are prone to seam pucker, seam type T1 get larger seam shrinkage than T4. Finally, the complex dynamic interactions was quantified and expressed through mathematical models.

Investigation into the color stripping of the pigment printed cotton fabric using the ozone assisted process: A study on the decolorization and characterization

Color stripping is one of the most convenient ways to rectify the various shade faults occurred during printing or dyeing process of textiles. But, the conventional chemical assisted process poses serious risk of the environmental pollution. Secondly, the chemical recycling of the cellulosic fibers may be disrupted due to the presence of the impurities like colorants, finishes, and the additives in the discarded textiles. So, there is a need to study ways to remove such impurities from the discarded cellulosic textiles in a sustainable manner. This work examines the decolorization of the pigment prints on cellulosic fabrics at pilot scale using an ozone-assisted process. The effect of varying pH, ozone concentration and the treatment time on the decolorization of the pigment prints was optimized using the response surface methodology technique. The effects of ozonation process parameters on the mechanical properties of cellulosic cotton fabric were measured. Decolorization of pigment printed samples was studied with respect to the surface effects by a scanning electron microscopy (SEM), and the chemical removal effects of ozonation treatment were studied using X-ray photoelectron spectroscopy. The possible mechanism regarding the action of ozone for the decolorization is discussed.

Thermal and breathability management of microencapsulated phase change material (MPCM) incorporated jute fabric

Jute bags are widely used to carry food grains and other materials that may be prone to quality deterioration due to thermal fluctuation. Thermal and moisture properties play a significant role in the packaging materials in the form of a container. This study deals with the effect of microencapsulated phase change material (MPCM) with hydrophobic binder on thermal and moisture management properties of jute fabric. Jute fabric was treated with MPCM by pad-dry-cure method. The treated sample was characterized by thermogravimetric analysis (TGA), differential scanning colorimeter (DSC), scanning electron microscope (SEM), moisture management tester (MMT), and air permeability tester. The results revealed that MPCM treated jute fabric shows greater thermal stability and heat absorption ability of 10.58 J/g while changing from solid to liquid phase. The SEM image ensures even distribution of MPCMs on fabric surface and surface roughness was also observed using image processing software. The air permeability was found to decrease whereas the water repellency enhanced in the developed sample.

Established an eco-friendly cotton fabric treating process with enhancing anti-wrinkle performance

This study establishes an eco-friendly anti-wrinkle treating process for cotton fabric. Sodium hydroxide-liquid ammonia pretreatment followed by 6% (w/w) PU100 adding citric acid pad-cure-dry finishing. In this process, citric acid (CA) was used as the fundamental crosslinking agent during finishing because it is a non-formaldehyde based, cost-effective and well wrinkle resistance agent. Environmental-friendly waterborne polyurethane (WPU) was used as an additive to add to the CA finishing solution. Six commercial WPUs were systematically investigated. Fabric properties like wrinkle resistance, tensile strength retention, whiteness, durable press, softness, and wettability were well investigated. Fourier transform infrared spectra and X-ray diffraction spectra were also measured and discussed before and after adding waterborne polyurethane. Tentative mechanism of the interaction among the WPU, CA, and modified cotton fabrics is provided. The effect of cotton fabric pretreatment on fabric performance was also investigated. After the eco-process’s treatment, the fabric wrinkle resistant angle was upgraded to 271 ± 7°, tensile strength retention was maintained at 66.77% ± 3.50% and CIE whiteness was elevated to 52.13 ± 3.21, which are much better than the traditional CA anti-wrinkle finishing based on mercerized cotton fabrics. This study provides useful information for textile researchers and engineers.

Cost optimization of sodium hypochlorite bleaching washing for denim by combining ensemble of surrogates with particle swarm optimization

Sodium hypochlorite bleaching washing process has been broadly carried out in denim garment industrial production. However, the quantitative relationships between process variables and bleaching performances have not been illustrated explicitly. Hence, it is impractical to determine values of the variables that can achieve the optimal production cost while satisfying the requirements of customers. This paper proposes an optimization methodology by combining ensemble of surrogates (ESs) with particle swarm optimization (PSO) to optimize production cost of chlorine bleaching for denim. The methodology starts from the data collections by conducting a Taguchi L25 (56) orthogonal experiment with the process variables and metrics for evaluating bleaching performances. Based on the data, the quantitative relationships are separately constructed by using RBFNN, SVR, RF and ensemble of them. Then, accuracies of the surrogates are evaluated and it proves that the ESs outperforms the others. Later, the production cost optimization model is proposed and PSO is utilized to solve it, while a case study is given to depict the optimization process and verify the effectiveness of the proposed hybrid ESs-PSO approach. Overall, the ESs-PSO approach shows great capability of optimizing production cost of sodium hypochlorite bleaching washing for denim.

Modeling the seam strength of denim garments by using fuzzy expert system

The main purpose of this study is to predict and develop a model for forecasting the Seam Strength (SS) of denim garments with respect to the thread linear density (tex) and Stitches Per Inch (SPI) by using a Fuzzy Logic Expert System (FLES). The seam strength is an important factor for the serviceability of any garments. As seams bound the fabric pieces together in a garment, the seams must have sufficient strength to execute this property even in the unexpected severe conditions where the garments are subjected to loads or any additional internal or external forces. Sewing thread linear density and number of stitches in a unit length of the seam are the two of the most important factors that affect the seam strength of any garments. But the relationship among these two specific variables and the seam strength is complex and non-linear. As a result, a fuzzy logic based model has been developed to demonstrate the relationship among these parameters and the developed model has been validated by the experimental trial. The coefficient of determination ( R2) was found to be 0.98. The mean relative error also lies withing acceptable limit. The results have suggested a very good performance of the model in the case of the prediction of the seam strength of the denim garments.

Cashmere and wool identification based on convolutional neural network

Wool fiber and cashmere fiber are similar in physical and morphological characteristics. Thus, the identification of these two fibers has always been a challenging proposition. This study identifies five kinds of cashmere and wool fibers using a convolutional neural network model. To this end, image preprocessing was first performed. Then, following the VGGNet model, a convolutional neural network with 13 weight layers was established. A dataset with 50,000 fiber images was prepared for training and testing this newly established model. In the classification layer of the model, softmax regression was used to calculate the probability value of the input fiber image for each category, and the category with the highest probability value was selected as the prediction category of the fiber. In this experiment, the total identification accuracy of samples in the test set is close to 93%. Among these five fibers, Mongolian brown cashmere has the highest identification accuracy, reaching 99.7%. The identification accuracy of Chinese white cashmere is the lowest at 86.4%. Experimental results show that our model is an effective approach to the identification of multi-classification fiber.

Review on the 3-D simulation for weft knitted fabric

There are different kinds of geometrical models and physical models used to simulate weft knitted fabrics nowadays, such as loop models based on Pierce, piecewise function, spline curve, mass-spring model, and finite element analyses (FEA). Weft knitting simulation technology, including modeling and yarn reality, has been widely adopted in fabric structure designing for the manufacturer. The technology has great potentials in both industries and dynamic virtual display. The present article is aimed to review the current development of 3-D simulation technique for weft knitted fabrics.