This paper examines the digital transformation of the textile and fashion industry, focusing on the alignment with sustainability principles through the integration of Industry 4.0 technologies. The introduction highlights the urgency of transitioning from conventional production methods to innovative, digitally enabled systems that promote a circular economy and resource efficiency. The main research questions address the contribution of Industry 4.0 elements to sustainable solutions, the directions of digitalization within the apparel sector, and the significant impact of digital technologies on the achievement of sustainability goals. The theoretical framework examines sustainability in the textile industry and emphasizes the need for a green transformation facilitated by digital technologies to reduce environmental impacts. Industry 4.0 concepts, as discussed in The Concept of Industry 4.0 in the Textile and Apparel Sector, are revolutionizing production through technologies such as IoT, AI, and blockchain, enabling traceability, customization, and energy-efficient operations. The paper also explores the evolution of the fashion and apparel industry into a high-tech sector, highlighting advances such as CAD-CAM systems, digital printing, and 3D technologies that improve precision, reduce waste, and support sustainable practices. In its conclusion, the paper emphasizes the crucial role of interdisciplinary collaboration, regulatory frameworks, and investment in skills development to overcome the challenges of implementing digital and sustainable practices. It posits that a strategic embrace of digital ecosystems and Industry 4.0 technologies is essential for creating a resilient and sustainable textile industry that is aligned with environmental and societal goals.

The main purpose of this research was to develop a method for the classification of body posture types, using a three-dimensional body scanner and data on anthropometric measurement. A sample of 102 male test subjects, aged from 20 to 30 years, and without structural deformities of the locomotor system, were scanned. Anthropometric body measurement was performed and 16 measurements were selected to calculate upper and lower body curve angles as a set of posture indicators. The number of maximally different groups of test subjects was determined using k-means cluster analysis and factor analysis of established posture indicators. The sampling into three upper and three lower posture types showed the largest statistically significant differentiation, comparing the results of variance analysis between and within the obtained groups, which confirmed three main components extracted by factor analysis. Discriminant analysis used in order to determine differences between posture types showed which indicators are the most important when classifying test subjects belonging to a particular upper or lower posture type. Classification functions were defined based on discrimination functions and their factor loadings, and used to calculate a matrix of correctly classified test subjects. The classification matrix showed very high prediction possibility of established posture indicators and the proposed method for body posture classification.

Contemporary CAD systems enable 3D clothing simulation for the purpose of predicting the appearance and behavior of conventional and intelligent clothing in real conditions. The physical and mechanical properties of the fabric and the simulation parameters play an important role in this issue. The paper presents an analysis of the parameters of the polygonal computer model that affect fabric drape simulation. Experimental research on physical and mechanical properties were performed for nine fabrics. For this purpose, the values of the parameters for the tensile, bending, shear, and compression properties were determined at low loads, while the complex deformations were analyzed using Cusick drape meter devices. The fabric drape simulations were performed using the 2D/3D CAD system for a computer clothing design on a disk model, corresponding to real testing on the drape tester in order to allow a correlation analysis between the values of drape parameters of the simulated fabrics and the realistically measured values for each fabric. Each fabric was simulated as a polygonal model with a variable related to the side length of the polygon to analyze the influence of the polygon size, i.e., mesh density, on the model behavior in the simulation. Based on the simulated fabric drape shape, the values of the areas within the curves necessary to calculate the drape coefficients of the simulated fabrics were determined in the program for 3D modelling. The results were statistically processed and correlations between the values of the drape coefficients and the optimal parameters for simulating certain physical and mechanical properties of the fabric were determined. The results showed that the mesh density of the polygonal model is an important parameter for the simulation results.

In the context of sustainable fashion, this paper presents research on the impact of property assessment methods of textile materials and pigment digital printing on the mechanical properties of fabrics and their 3D simulation in the development of digital prototypes for clothing design. Six woven fabrics, with and without a textile ink-jet print, were tested using a KES-FB measuring system and digitized using SEDDI Textura AI technology. The determined mechanical parameters were used for 3D draping simulations based on Cusick Drape Meter method, as well as for the simulation of frilly women’s skirt models. The research showed a good correlation between the draping of real fabric samples and their 3D simulations, particularly supporting the use of AI for fabric assessments due to its sustainability. The drape analysis, performed on the digital 3D prototypes of a frilly women’s skirt model in two different lengths, showed the influence of fabric ink-jet printing on the drape properties, which can be explained by some structural parameters and determined changes in mechanical parameters between unprinted and printed fabric samples. The results provide valuable insights for objective evaluation of clothing digital 3D prototypes, which is a very significant element in the production process from a sustainability point of view and is becoming increasingly prominent as a method for developing new clothing designs that is gradually replacing the traditional, less environmentally friendly approach of creating numerous physical test samples.