The ability to eliminate contaminants from the surface without an external source is a characteristic feature of self-cleaning textile fabrics. This advanced application of science is environmentally friendly because it saves on laundry, water, electricity, and other resources and money. In the past, the self-cleaning feature was typically acquired through chemical coatings, which enhance the roughness and lower the surface energy of the fabric, thereby causing the particle or droplet pollutants to float over the surface layer instead of adhering to it. This technique works well for fabrics manufactured using conventional woven-based textile technology. The production of textile fabrics has changed recently, thanks to paid advances in technologies such as 3D printing. However, the earlier chemical coating-based technique cannot clean printed fabrics in such a way. A developed regression model to study the relationship between the secondary 3D printing parameters and the self-cleaning capabilities of various polymeric textiles allowed a deeper investigation. This study aims to examine the effects of the primary printing factors, such as the flow rate, printing temperature, and speed printing, on the self-cleaning characteristics of the printed textile. We measured the self-cleaning behavior of the chosen material, polyurethane fabric, used the trial findings to build a regression polynomial, and empirically validated models to show the critical values of the main parameters considered for the ideal self-cleaning behavior. The outcome indicated the processing-property relationship of the parts, including determining the most vital parameters that influence the fabric’s wettability. |
