Human body measurement based on two-dimensional images has been widely applied in the clothing industry due to its cost and operational advantages. However, the current accuracy of human body circumference measurement is low. This article aims to propose a high-precision method for measuring human body circumference, taking bust circumference and waist circumference as examples, and based on 120 virtual simulations of human bodies, proposes a method to extract human body bust circumference size from front and side angles images. Using the feature value pixel size to calculate the trapezoid perimeter and the ellipse perimeter, and comparing them with the difference of bust circumference and waist circumference sizes, machine learning is applied to build a size prediction model, thus obtaining the values of bust circumference and waist circumference. The experimental results show that the average prediction errors of bust girth and waist girth by the proposed method are 0.26 cm and 0.24 cm, respectively, indicating good prediction performance and applicability for practical production. The proposed method effectively reduces the measurement errors of girth dimensions in image measurement and provides methods and ideas for non-contact human body measurement research.

This study aimed to investigate the impact of varying fibre compositions and fabric constructions on the elastic behaviour of compression fabrics. Knitted fabrics used as compression garments are subjected to deformation due to various loads used to extend the fabrics during wear. The loads to extend the fabrics are influenced by fabric constructions, strain levels and fabric directions. In this study, the strain of fabrics was measured using a commercial tensile testing machine (LR30K Lloyd) according to ASTM D496. Each sample was cycled five times between zero and the specified strain to replicate the repeated use of compression garments. In addition, the fabric was also heat-set and laundered to evaluate the impact of these treatments on the elastic behaviour of the fabric. The results indicated that different fabric constructions exhibited different elastic properties $(P<0.05),$ with $1×1$ rib fabric demonstrating the highest load in the wale direction and terry fabric displaying the highest load in the course direction. Fibre composition in single jersey fabrics significantly affected $(P<0.05)$ their load requirements for extension, with 8% elastane displaying the highest resistance to stretching. Heat-setting positively affected the load capacity of the fabrics (24%-47%), enhancing the dimensional stability and strength. However, laundering after heat-setting decreased the load capacity (6%-32%), negatively affecting fabric deformation and shape retention. These findings aim to lay the groundwork for the importance of careful selection of fabric attributes and post-processing treatments for optimising the elastic properties of compression fabrics.

As technology continues to advance, there is a growing interest in personalized customization with diverse styles and a high degree of fit. To address challenges such as long production cycles and high labor and material costs associated with personalized customization, there has been significant research on automatic pattern generation. However, most of these studies focus on relatively single garment styles. Therefore, this paper proposes a method for automatically generating multi-style collar patterns. First, by analyzing the characteristics of stand collar styles, the modules of stand collar are determined, and the control attributes and methods of each module are determined according to the actual needs. Based on this, a modular design method for stand collar is constructed; the neck parameters and stand collar structure are statistically analyzed, and a mapping model between neck parameters and stand collar structure is established; then, the relationship between stand collar modules and paper patterns is analyzed, and a relationship model between numerical control modules and paper pattern parameters is established to achieve the purpose of driving stand collar structure parameters by stand collar styles; then, according to the stand collar structure design method, the key points of the pattern are parameterized, thus realizing the parametric design of the stand collar pattern; finally, using Matlab software, different components are coordinated, and the modular design method and parametric design of stand collar are comprehensively applied to realize the automatic generation of different styles of stand collar patterns. The research shows that the automatic pattern generation method established in this paper can meet the automatic pattern generation of different stand collar styles, and reduce some human and material costs for the pattern making process in the clothing industry.

In today’s fast-paced life, ensuring food’s freshness, safety, and optimal nutritional and economic value is paramount for both consumers and businesses. Textiles have played a significant role in the food preservation industry throughout history. With the continuous development of the textile and preservation industries, novel methods have been explored to enhance the effectiveness of preservation. This article provides a comprehensive overview of textiles’ performance requirements and limitations in the food preservation process. Additionally, it introduces innovative techniques and recent advancements in textile materials designed explicitly for preservation purposes. Furthermore, the paper highlights the key challenges that must be addressed in future research. Consequently, this scientific review serves as a valuable reference for the application and advancement of textiles in the dynamic field of food preservation.

Currently, line production is the mainstream production method for various clothing enterprises. Therefore, optimising and improving the production line plays a crucial role in promoting the development of manufacturing enterprises. To solve the problems of unbalanced operation time and low production line balance rate of each workstation of the garment sewing production line, a multi-objective optimisation mathematical model with the minimum smoothing index and the largest production line balance rate was established, and the dual-population genetic algorithm was designed in the MATLAB environment. The jeans (front piece) were used as an example to be simulated and verified in simulation software. Achieve load balancing at workstations, save production costs, and eliminate overproduction between jobs. The research results show that the smoothness index of the optimised production line has been reduced from 20.89 to 8.43, and the production line balance rate has been increased from 77.57% to 89.06%, which meets the requirements of enterprise process planning and can deliver on time. This verifies that the model proposed in this paper can effectively solve the production balance problem of a single clothing production line.