Experimental investigation of self-cleaning behaviour of 3D-printed textile fabrics with various printing parameters

Self-cleaning of textile fabrics is defined as the ability that the pollutants particles can be removed from the fabric surface without any external source. The application of the technology is beneficial to the environment since it conserves water, energy and laundry costs. In the past, it is typically obtained by chemical coatings, which develop low surface energy and high roughness on the fabric surface, allowing the pollutant particles or droplets to float over the surface rather than adhesion. These chemical coating methods are effective for fabrics manufactured by traditional woven-based textile technology. However, the recent advancements in 3D printing technology have evolved the manufacturing of textile fabrics but with equal challenges in self-cleaning as previous chemical coating-based methods are not useful for printed fabrics. A recent study has successfully established a linear regression model to demonstrate the relationship between secondary 3D printing parameters and the self-cleaning properties of different polymeric fabrics. This paper is intended to analyse the impact of the primary printing parameters on the self-cleaning attributes, including infill rate (IR), flow rate (FR), printing temperature (PT), printing speed (PS), and printing acceleration (PA). The experimental results were used to construct a regression polynomial to quantify the self-cleaning behaviour of the selected thermoplastic polyurethane (TPU) fabric. The models were validated experimentally to highlight the critical values of considered primary parameters for optimal self-cleaning behaviour. The obtained results indicated that FR was the most significant parameter, and all parameters affected the fabric's wettability almost equally.