Coloration Technology最新文献

Coordination complexes were widely used in advanced oxidation processes. The ligands with various substituents could lead to differences in the catalytic properties and mechanisms. In this work, the iron(III)–N,N′-dipicolinamide (FeL) complexes (the iron(III) complexes with substituents –CH₃, –H, –Cl and –NO₂ named as FeL1, FeL2, FeL3 and FeL4, respectively) were used to activate hydrogen peroxide (H₂O₂) for degrading dyes wastewater. Mechanism studies indicated that the FeL4/H₂O₂ system contains the Fe^V=O in addition to the same ^•OH and O₂^•− as the other systems, which made it exhibit more excellent performance than others. The results of the performance tests showed that the FeL4/H₂O₂ system could remove 97%, 89%, 100%, 83%, 100%, and 99% of RR195, RY145, RB194, RB19, MB, and RhB, respectively, which proved the good application performance of the FeL4/H₂O₂ system. In addition, the performance of the FeL4/H₂O₂ system was not influenced by anions and natural organics. This study verified the feasibility of modulating the catalytic performance of the complexes by changing the substituents and provided an efficient catalytic system for dyeing wastewater treatment.

A full gamut colour solid model consisting of three lightness planes, 18 colour mixing units and 360 grid points is constructed from nine primary coloured fibres: red (R), yellow (Y), green (G), cyan (C), blue (B), magenta (M), dark grey (O¹), medium grey (O²) and light grey (O³). Subsequently, the 213 coloured yarns and fabrics containing different lightness, hue and saturation were prepared according to the mixing ratio parameters in the colour solid. The Stearns–Noechel colour prediction algorithm, which predicts reflectance using coloured fibre mixing ratios, was improved and applied according to the requirements of colour prediction; and the Stearns–Noechel proportion prediction algorithm, which predicts coloured fibre mixing ratios by reflectance, was refined and employed in accordance with the demands of proportion prediction. Then, the 12 additional coloured fabrics were fabricated and their corresponding measurement data were used on the algorithm for validating its forecasting capabilities. The final experimental results reveal that the maximum colour difference for colour prediction is 5.5, the minimum is 1.7, and the average is 3.7; the maximum colour difference for proportion prediction is 3.3, the minimum is 0.3, and the average is 1.6. Therefore, this approach is promising to improve the colour reproduction issues encountered in the processing of three-channel computer numerical control (CNC) spinning.

To address the issue of low precision in classifying the colour differences of yarn-dyed fabrics and the high cost of manual detection, a colour difference classification method relying on an improved seagull optimisation algorithm (SOA) optimised regularised random vector functional link (RRVFL) model is proposed for dyed fabrics. First, to address the issue of the slow convergence speed of the SOA, the current study optimises the initial SOA group with the marine predators algorithm (MPA) so that it can effectively improve the convergence ability and global optimisation ability of the SOA. Subsequently, the enhanced SOA is applied to fine-tune the parameters of the RRVFL. Compared with the methods that only optimise weights and bias, the proposed algorithm obtained by optimizing the initial group of SOA through the Marine Predators Algorithm (MSOA)-RRVFL model in this paper also increases the optimisation of the number of nodes in the hidden layer and regularisation parameters, which also effectively avoids the issue of the low classification accuracy of the RRVFL model due to random related parameters. Finally, by comparing the RRVFL model with other optimisation algorithms, the experimental outcomes demonstrate that the convergence ability of the improved SOA has been improved, and that the average accuracy of colour difference classification by the MSOA-RRVFL model is as high as 99.79%, and that the classification error fluctuation can be stabilised below 0.2%. In general, the MSOA-RRVFL model displays an excellent performance in terms of stability and significance.

According to the demand for colour prediction for coloured yarn, two adjacent colours chosen from red (R), yellow (Y), green (G), cyan (C), blue (B) and magenta (M) fibres were combined with fibres of dark grey (O¹), medium grey (O²) and light grey (O³), respectively, and then ternary coupling-superposition mixing was performed to acquire a colour solid consisting of three lightnesses, 18 colour mixing units and 18 × (m + 1) × n grid points. An integrated colour mixing with 20% hue gradient and 33.33% saturation gradient was performed to achieve a colour solid containing 360 grid points, then using it as the sample space for the colour prediction model. A total of 360 typical samples were established by the grid points, 213 yarns and fabrics were prepared by the typical sample parameters, and the corresponding reflectance was accessed by a spectrophotometer. Neural network models for predicting reflectance by mixing ratios as well as forecasting mixing ratios by reflectance, were established. The 12 non-grid point parameters were chosen to prepare corresponding yarns and fabrics, and the corresponding reflectance was measured. The predicted and measured values of the neural network model were compared to verify its predictive ability and generalisability. The results showed that when predicting the colour by the mixing ratios, the colour difference between the predicted and measured samples ranged from 1.5 to 3.4, with an average of 2.4; and when forecasting the mixing ratios by the colour, the colour difference ranged from 0.8 to 5.6, with an average of 2.4.

Aiming at the defects in the process of fabric production, a defect detection model of fabric based on a mixed-attention-based multi-scale non-skipping U-shaped deep convolutional autoencoder (MADCAE) was proposed. In a traditional encoder, the convolutional layer treats each pixel equally, so the importance of different pixels cannot be reflected. It is difficult to obtain richer and more effective information. The reconstruction of the defect region and the detection of the defect region are further affected. In this article, three different scale features of input images are extracted by enlarging the receptive field with large kernel convolution blocks. A hybrid attention module is used to ensure the richness of the extracted information in terms of space and channel. Experiments show that this method can effectively reconstruct fabric parts without requiring a large number of defect marking samples. It can quickly detect and locate defective areas of fabric patterns.

Although the impact of smoking tobacco on human health is well understood, less is known about the effects of tobacco smoke on cotton, viscose and polyamide fabrics. In this study, tobacco smoke was applied to fabric samples to investigate the effects of tobacco smoke on their mechanical and colour properties. For this purpose, tobacco smoke was pumped into a mechanism consisting of a glass box, in which cotton, viscose and polyamide fabrics were placed in a suspended position. The fabric samples were treated with tobacco smoke for 1 or 2 months. The samples were evaluated in terms of tensile and tear strength, elongation at break, as well as pilling and abrasion resistance values. A colour measurement test was used to investigate the withering effect of tobacco smoke, and Fourier Transfer–Infrared analysis was performed to examine the chemical changes. The tensile strength values in the warp direction were 419.34, 404.62 and 421.78 N without treatment and after 1 and 2 months of tobacco smoke treatment, respectively, for the cotton woven fabric. Furthermore, for woven cotton fabric, the L* value decreased from 93.8 to 78.7 after being treated with tobacco smoke for 2 months. As a result of this study, it was determined that tobacco smoke has no effect on the tensile strength properties of fabrics, causes changes to pilling and abrasion resistance values, and adversely affects the colour properties of fabrics.

In this study, it is aimed to provide alternative fibres to cotton with enhanced comfort level, environmentally friendly and colour-enriched in the cellulosic knitted fabric field. For this purpose, in addition to 100% cotton, 100% linen, 100% hemp yarns, knitted fabrics were produced from 70% cotton/30% linen and 70% cotton/30% hemp yarns. First of all, the properties of yarns such as tenacity, elongation at break, yarn unevenness, thin places, thick places and neps were examined comparatively. Then, pique fabrics were produced from these yarns and dyed with a reactive dye to a selected colour. Afterwards, physical (weight, wale/course density), mechanical (bursting strength, pilling, abrasion resistance) and comfort (air permeability and water vapour permeability) properties of all fabric samples, both in raw form and after dyeing and finishing processes, were compared. Furthermore, dyeing properties (colour, dye-uptake, dyeing levelness, fastness) of fabric samples were also investigated. The dye uptake (%) values of the yarns decrease in the order of cotton > cotton/hemp > cotton/linen > hemp > linen. However, fastness values of dyed fabrics were nearly identical. Physical and mechanical properties of fabrics were very similar, while the air permeability of the fabrics decrease in the order of hemp > linen > cotton/hemp > cotton/linen > cotton. As a result of the study, it has been possible to produce knitted fabrics with superior performance characteristics (dyeability, comfort, etc.) from yarns produced via blending natural cellulosic fibres (flax and hemp) with certain proportions of cotton fibres, which contribute to sustainable production.

Regenerated protein fibres manufactured from food side-streams offer significant potential as circular and sustainable fibres, but greater knowledge of their dyeing properties is required. In this research, coloration of casein fibres with dyes also extracted from blackcurrant skins left over from juice pressing is explored. Casein fibre was dyed with blackcurrant extract, rich in anthocyanins, from pH 2 to pH 6 and from 40 to 80°C, with and without alum. Casein fibres could be dyed with blackcurrant extract across all conditions tested, and under optimal conditions, dyeing is achieved with medium depths of colour with good wash fastness. Highest sorption of anthocyanins onto casein is observed at pH 4, where anthocyanins are a mixture of 60% neutral purple quinonoidal base form and 40% flavylium cation form; under these conditions dye–fibre interaction is optimal. At pH 2, casein fibre has a highly positively charged surface and anthocyanin is in the flavylium cation form, leading to some dye–fibre repulsion. At pH 6, the slightly negatively charged casein fibre demonstrates lower sorption of the mixture of 40% purple quinonoidal base form and 60% the anionic quinonoidal base form, again leading to some dye–fibre repulsion. Presence of alum in the dyebath enhances sorption of anthocyanins onto fibre at pH 4 due to formation of Al–anthocyanin complexes. Wash fastness of the dyeings is better as pH increases and as temperature increases.

This review concerns the application of disperse dyes to poly(ethylene terephthalate) fibres using aqueous immersion dyeing processes and the roles of both elevated dyeing temperatures and carriers in the dyeing system. The precise reasons why very high temperatures in the region of 130°C promote the uptake of disperse dyes on poly(ethylene terephthalate) fibres from aqueous dyebaths have not been satisfactorily resolved, nor has the exact mechanism by which carriers promote dye uptake at lower temperatures in the region of 98°C been adequately established. In this part of the review series, a detailed review and analysis is presented of the various concepts and theories that have been proposed to account for the promotional effects of temperature and carriers on disperse dye uptake, from the viewpoint of both the thermodynamic and kinetic aspects of the adsorption of the dyes on poly(ethylene terephthalate) and other types of fibre.

Detection of defects is an essential quality control method in fabric production. Unsupervised deep learning-based reconstruction algorithms have recently been deeply concerned owing to scarce fabric defect samples, high annotation cost, and deficient prior knowledge. Most unsupervised reconstruction models are prone to overfitting and poor generalisation performance, resulting in blurred images, residual defects, and uneven textures in the reconstruction results. On this account, an unsupervised fabric surface defect detection method using the Progressive Mask Repair Model (PMRM) has been developed. Specifically, PMRM with transformer architecture gathers detailed feature information. In order to pay closer attention to the textural properties of fabrics, the model incorporates structural similarity as a constraint in the training stage. In the detection stage, we designate the non-defective area of the fabric image as the background and the defective area as the foreground. Next, a progressive mask is applied to repair the background of the defective area, which avoids defect false detection resulting from the poor reconstruction effect of the traditional reconstruction model in the non-defective area. Finally, image processing methods such as image difference, frequency-tuned salient detection, and threshold binarisation are used to segment the defects. Relative to the other six unsupervised defect detection methods, the proposed scheme increases the F1 score and intersection over union (IoU) by at least 9.34% and 8.49%, respectively. According to the earlier results, PMRM is effective and exhibits superiority.