Aqueous polyurethane dispersion (PUD) has attracted increasing attention in a wide range of industrial applications because of their versatile properties as well as ecofriendly nature. In this study, the aqueous PUD used in warp-knitted vamp printing was characterized by Fourier transform infrared spectra, dynamic light scattering, and laser Doppler electrophoresis. The mean diameter and zeta potential are 206.6 nm and −18.3 mV, respectively. The rheological behavior of aqueous PUD as a function of shear rate, temperature, and solid content was investigated experimentally. Besides, a new correlation model was proposed based on the Carreau equation and Arrhenius relation. The resulting model has high accuracy in viscosity estimation under complex conditions according to the prediction interval of 95%. Furthermore, the reasonable ranges of parameters were proposed theoretically for successful printing.

This paper presents a hybrid intelligence technique based on the Taguchi method for multi-objective process parameter optimization of 3D additive screen printing of athletic shoes. 3D additive screen printing is mainly used in the high-end athletic shoes and clothes field. It requires overlapping and overprinting dozens of times to make the printed patterns stereoscopic. The process of 3D additive screen printing is complex and variable and the production cycle is long. Because of the variability of the screen printing process and the coupling between process parameters, there is no simple method to guide the trial production of new products and obtain the optimal process parameters of screen printing. Trial-and-error is often used but it expends a lot of manpower, materials, and financial resources. To solve the optimization problem, a Taguchi experiment based on fuzzy comprehensive evaluation with five factors and two responses was first designed. Then, a back-propagation network (BPN), least-squares support-vector machine (LSSVM), and random forest (RF) were trained with experimental data to obtain a forecasting model for the process parameters. On comparison, the RF forecasting model performed best in this case. Then, the multi-objective antlion optimizer (MOALO), which is a new multi-objective optimization algorithm with excellent performance, was improved to the IMOALO, and it was proved that IMOALO has a better performance than MOALO. Combining the RF forecasting model with IMOALO, and carrying out the optimization, the optimal process parameters were obtained. Actual printing production shows that the proposed hybrid intelligence technique improves the production efficiency and first pass yield of printed products.

Super-hydrophobic fabrics have shown great potential during the last decade owing to their novel functions and enormous potential for diver’s applications. Surface textures and low surface energy coatings are the keys to high water repellency. However, the toxicity of nanomaterials, long perfluorinated side-chain polymers, and the fragile of micro/nano-texture lead to the super-hydrophobic surfaces are confined to small-scale uses. Thus, in this article, a stable polydimethylsiloxane (PDMS)-coated super-hydrophobic poly(ethylene terephthalate) (PET) fabric (PDMS-g-PET) is manufactured via dip-plasma crosslinking without changing the wearing comfort. Benefiting from the special wrinkled structure of PDMS film, the coating is durable enough against physical abrasion and repeated washing damage, which is suffered from 100 cycles of washing or 500 abrasion cycles, and the water contact angle is still above 150°. This study promotes the way for the development of environmentally friendly, safe, and cost-efficient for designing durable superhydrophobic coatings for various practical applications.

Purpose In 3D additive screen printing with constant snap-off, the inhomogeneous screen counterforce will influence the printing force and reduce the printing quality. The purpose of this paper is to study the relationship between scraper position, snap-off and screen counterforce and develop a variable snap-off curve for 3D additive screen printing to improve the printing quality. Design/methodology/approach An experiment was carried out; genetic algorithm (GA) optimization theoretical model, backpropagation neural network regression model and least square support vector machine regression model were established to study the relationship between scraper position, snap-off and screen counterforce. The absolute errors of counterforce of three models with the experiment results were less than 1.5 N, which was tolerated and the three models were considered valid. The comparison results showed that GA optimization theoretical model performed best. Findings The results suggest that GA optimization theoretical model performed best to represent the relationship, and it was used to develop a variable snap-off curve. With the variable snap-off curve in 3D additive screen printing, the inhomogeneous screen counterforce was weakened and the printing quality was improved. Originality/value In printing production, the variable snap-off curve in 3D additive screen printing helps improve the printing quality; this study is of prime importance to the 3D additive screen printing.

Smart textiles have a wide range of applications in all walks of life. Wearable sensors have become the main research hotspot due to their excellent performance in both perception and response. In this paper, polyvinylidene fluoride (PVDF) was used as the raw material to fabricate continuous fiber via melt spinning method, and β-phase crystal PVDF fiber was obtained at a melting temperature of 230°C and draft ratio of 5. The plain weave fabric of PVDF fiber was dipped into hot dimethyl silicone oil for thermal polarization treatment. When the plate was at a distance of 4 mm, the polarization temperature was 90°C and the polarization voltage was 9 kV, the PVDF had the best polarization property. After polarization, the PVDF fabric showed a good piezoelectric response and a PVDF flexible sensor could be applied to chemical fiber and natural fibers fabrics, in human health monitoring equipment in contact with close-fitting clothing or in home textile products for intelligent alarm systems.