Owing to global warming and pollution concerns, reducing the environmental footprint of the textile and fashion industry has received considerable attention. Within this industry, the dyeing and finishing processes contribute significantly to greenhouse gas emissions and water pollution. This study introduces an innovative approach to address these challenges by leveraging digital twin technology to optimize the textile dyeing process. A smart analysis module was developed to continuously monitor and analyze the dyeing parameters in real time to implement control actions to automatically reduce the process duration. Integrated with this module, a digital twin of the dyeing machine enabled the real-time monitoring of energy consumption and process parameters. A case study comparing the traditional dyeing process with the optimized process was conducted. The results showed that dyeing time was reduced by ~ 17.5% without compromising dyeing quality. Energy consumption and greenhouse gas emissions were also reduced by ~ 12.1% when using the optimized process. This study offers a practical and sustainable option for textile dyeing, particularly for small and medium-sized enterprises.

Vast amounts of dyeing wastewater released from the textile industry can not only cause water pollution but also have negative effects on the human body, such as skin irritation and respiratory diseases. Dye adsorption technology is necessary for the treatment of wastewater discharged from the dyeing industry and for environmental improvement. However, to remove dyeing wastewater, more energy and solvents are used to fabricate adsorbents, or excessive energy is used to filter dyeing wastewater out, resulting in more environmental pollution. Therefore, it is necessary to develop a method of filtering dyeing wastewater in a more environmentally friendly manner by minimizing the use of solvents and energy. In this study, we modified the surface of a textile substrate through UV irradiation to create a monomer capable of facilely bonding with dyes. Employing the UV photografting method, we were able to produce a dye adsorption filter in a more environmentally friendly manner, minimizing solvent usage and heat energy consumption required for absorbent synthesis. At a monomer concentration of 10%, the fabricated filter exhibited a dye removal efficiency of 97.34% after 24 h, all without the need for a pressure treatment or temperature increase. Moreover, it displayed an adsorption capacity of approximately 77.88 mg per 1 g of filter material.

Purpose: The purpose of this paper is to develop a versatile and extensible three-dimensional (3D) body measurement system. Design/methodology/approach: An integrated development environment and a script language compiler were designed for easy definition of measurement process. Findings: Researchers can do every kind of anthropometric research with respect to gender, age or race simply by writing appropriate scripts. Research limitations/implications: System does not support the management of 3D data itself. The possible flaws of data should be removed by other hardware specific software. Practical implications: Researchers without comprehensive knowledge of computer programming can conduct complex anthropometric research by simply learning the concise script language. Social implications: This system will facilitate many anthropometric research works and will help to provide valuable information to many industries. Originality/value: This is the first script language based body data analysis system. The script language consists of about 60 simple commands. Researchers will be able to concentrate more on the research topic itself than on the painstaking computer programming.

Purpose: The purpose of this study is to introduce a dedicated simulator to automatically generate and simulate a balanced apparel assembly line, which is critical to the digital twin concept in apparel manufacturing. Given the low automation level in apparel manufacturing, this is a first step toward the implementation of a smart factory based on cyber-physical systems.Design/methodology/approach: The mixed task assignment algorithm was implemented to automatically generate a module-based apparel assembly line in the developed simulator. To validate the developed simulator, a case study was conducted using process analysis data of technical jackets obtained from an apparel manufacturer. The case study included three scenarios: calculating the number of workers, selecting orders based on factory capacity and managing unexpected worker absences.Findings: The developed simulator is approximately 97.2% accurate in assigning appropriate tasks to workstations using the mixed task assignment algorithm. The simulator was also found to be effective in supporting decision-making for production planning, order selection and apparel assembly line management. In addition, the module-based line generation algorithm made it easy to modify the assembly line.Originality/value: This study contributes a novel approach to address the challenge of low automation levels in apparel manufacturing by introducing a dedicated simulator. This dedicated simulator improves the efficiency of virtual apparel assembly line generation and simulation, which distinguishes it from existing commercial simulation software.