Personalization in the apparel industry shows importance and the potential for demand, but the existing personalization has unreasonable time cost, labor cost, and resource waste. To solve the problems of the waste of resources as well as both time and labor cost caused by manual pattern making in clothing personalization, a method of automatic garment pattern generation based on a parametric formula and the Python language was proposed. Based on the classification of common curves in patterns, three curve fitting algorithms based on different parameters were derived and combined with the Python language to achieve personalized generation of different patterns by classifying the parameters in the system into key parameters, secondary parameters, and variable parameters. Three different methods for verifying the accuracy of the garment patterns were proposed based on curve fitting similarity and three-dimensional virtual modeling, and the accuracy of the proposed system was verified. The results show that the accuracy and comfort of the patterns generated via the system were high. Meanwhile, the Python-language-based system fits well with the production system of enterprises, which can improve the rapid response capability of garment personalization, greatly save the time cost and labor cost of enterprises, reduce resource loss, and contribute to the sustainable development of the garment industry.

Waste recycling is an effective way to improve waste management, which is closely related to the support of social and economic foundations. With the development of a circular economy, green consumption is imperative. Most of the environmental protection brand enterprises are now almost limited to the environmental protection of clothing raw materials. However, there are still many problems in the overall industrial chain of the clothing industry, such as the pollution in the processes of processing, transportation and laundry, and the waste of resources caused by a large amount of textile waste after consumption. Starting from the theme of environmental protection and sustainable development of the clothing industry, this paper discusses the necessity of building a recycling system for post-consumer textile waste. Through the investigation of the recycling and reuse of domestic post-consumer textile waste, the existing problems are analyzed, such as the recycling supervision mechanism’s imperfections, the trust crisis and a lack of recycling channels. Combined with the successful cases abroad, some solutions and suggestions are put forward for the regeneration and reuse of post-consumer textile waste, and a preliminary conception of the charitable market system is made.

In sportswear industry, the usage of synthetic material is increasing due to its excellent water transport characteristic. This study aims to evaluate the thermo-physiological comfort properties of different sports fabrics and to investigate the effect of fabric physical property and knitting parameter on comfort-related properties of commercial sportswear fabric. Vertical wicking test, wettability test, dry rate test as well as air resistance test were conducted on 17 types of polyester-knitted fabrics obtained from market. Analysis of variance and multiple-comparison least significant difference test was performed to select the best fabric for sportswear. A single jersey fabric sample No. 16 made of 75D/100f in 145 g/m2 as determined to be the best fabric for sports T-shirt due to its excellent wicking property, faster water absorbency, quicker dry rate, and lower air resistance. It is likely that finer filament, low fabric mass, and proper density are associated with best property. Besides, it is found that higher mass was associated with higher air resistance, higher porosity was associated with faster dry speed, and faster vertical wicking rate was associated with shorter water absorption time for fabrics tested in this study.

Skin wetness and body water loss are important indexes to reflect the heat strain of the human body. According to ISO 7933 2004, the skin wetness and sweat rate are calculated by the evaporative heat flow and the maximum evaporative heat flow in the skin surface, etc. This work proposes the soft textile-based sensor, which was knitted by stainless steel/polyester blended yarn on the flat knitting machine. It investigated the relationship between electrical resistance in the weft/warp directions and different water absorption ratio (0–70%), different sample size (2 cm × 2 cm, 2 cm × 4 cm, 2 cm × 6 cm and 2 cm × 8 cm). The hydrophilic treatment effectively improved the water absorption ratio increasing from 40% to 70%. The weft and warp direction exhibited different electrical behaviors when under dry and wet conditions. It suggested the weft direction of knitted fabrics was recommended for detecting the electrical resistance due to its stable sensitivity and linearity performance. It could be used as a flexible sensor integrated into a garment for measuring the skin wetness and sweat rate in the future instead of traditional measurements.