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.

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.

With the rapid development of clothing online shopping, customers have a higher demand for more accurate clothing size recommendation systems. The work this paper presented is compatible with the four body types specified in the Chinese standard. Then, the subdivision was performed where each of the major body types corresponded to four characteristic indices. In addition, each characteristic index had three attributes. Therefore, the female body can be subdivided into 81 types for each major type. Subsequently, this paper suggests an improved clothing size recommendation system that considers subdivided body types. The recommendation criteria in the approach were refined by a pressure distribution analysis through a virtual fitting simulation. Finally, case studies were conducted on customers of body type A (accounting for 52.73% of bodies) to demonstrate the procedures of the proposed approach. Practitioner summary: This study proposes an improved clothing size recommendation approach based on the subdivision of code-specified female body types. Compared with existing clothing size recommendations, the proposed approach can help customers find formfitting sizes more accurately.