Characterization of 3D-printed filament yarns using various parameters

Abstract

Smart clothing refers to clothing that is enhanced with technology to add functionality beyond its traditional use. It capable of perception and response it can mimic the characteristics of living systems. Conductivity in textiles is essential for smart clothing because electrical conductivity provides pathways for carrying information or energy for various functions. Textile conductivity can be imparted at various stages. Conductive polymers, fibers, yarns, fabrics, embroidery, and finishing are vital for the construction of smart clothes. most smart materials, electronic sensors or actuators are used to integrate the desired interactions with the environment. Typical textile methods, such as sewing, necessitate the adequate construction of electronic components, for example, by first soldering an electronic component on a flexible substrate with holes that can then be used for a sewn connection to a conductive yarn in a textile fabric. Most currently commercialized 3D printing processes involve fused deposition modeling (FDM) methods, which extrude thermoplastic filaments through nozzles. This process is similar to melt spinning, and modeling to print lines using one layer can yield a filament yarn shape. 3D printing outputs molten polymers by discharging thermoplastic filaments though a nozzle by an extrusion process. This method is similar to melt spinning. Currently, various types of materials are used in 3D Printing, However not all of these materials are suitable for textile applications. In addition, dogbone-type outputs were used to evaluate physical properties in previously reported studies in which the parameters of existing 3D printing were changed. but unlike filament-type outputs, dogbone-type outputs are composed of multiple layers rather than single layers. therefore, their properties are different from those of single-layer filament outputs. This study, as a basic study to develop smart textiles using 3D printing, herein we aimed measuring and analyzing the impact of conditions like printing speed and nozzle temperature on the surface structure, crystallinity, and mechanical properties of the outputs. Therefore, unlike dogbone-type sample forms, which have been used in many previous 3D-printing property evaluation studies, there is no adhesion between layers as the temperature increases. Therefore, the physical properties do not increase. This study entailed research to develop smart textiles using FDM-type 3D printing. A follow-up study that aims to evaluate the 3D printing spinning properties that combine extrusion 3D printing and melt spinning spools is underway.