Although direct deposition of polymeric materials onto textiles through 3D printing is a great technique used more and more to develop smart textiles, one of the main challenges is to demonstrate equal or better mechanical resistance, durability and comfort than those of the textile substrates before deposition process. This article focuses on studying the impact of the textile properties and printing platform temperature on the tensile and deformations of non-conductive and conductive poly lactic acid (PLA) filaments deposited onto polyethylene terephthalate (PET) textiles through 3D printing process and optimizing them using theoretical and statistical models. The results demonstrate that the deposition process affects the tensile properties of the printed textile in comparison with the ones of the textiles. The stress and strain at rupture of the first 3D printed PLA layer deposited onto PET textile material reveal to be a combination of those of the printed layer and the PET fabric due to the lower flexibility and diffusion of the polymeric printed track through the textile fabric leading to a weak adhesion at the polymer/textile interface. Besides, printing platform temperature and textile properties influence the tensile and deformation properties of the 3D printed PLA on PET textile significantly. Both, the washing process and the incorporation of conductive fillers into the PLA do not affect the tensile properties of the extruded polymeric materials. The elastic, total and permanent deformations of the 3D-printed PLA on PET fabrics are lower than the ones of the fabric before polymer deposition which demonstrates a better dimensional stability, higher stiffness and lower flexibility of these materials.
Purpose This paper aims to evaluate and simulate the impact of the build platform temperature of the three-dimensional (3D) printer, the structure and heat transfer of textiles on the adhesion and durability after washing properties of 3D printed polymer onto textile materials using thin layers of conductive and non-conductive extruded poly lactic acid monofilaments (PLA) deposited on polyethylene terephthalate (PET) woven fabrics through fused deposition modeling (FDM) process. Design/methodology/approach Prior to FDM process, thermal conductivity, surface roughness and mean pore size of PET woven fabrics were assessed using the “hot disk,” the profilometer and the capillary flow porometry methods, respectively. After the FDM process, the adhesion and durability after the washing process properties of the materials were determined and optimized based on reliable statistical models connecting those properties to the textile substrate properties such as surface roughness, mean pore size and thermal conductivity. Findings The main findings point out that higher roughness coefficient and mean pore size and lower thermal conductivity of polyester woven textile materials improve the adhesion properties and the build platform presents a quadratic effect. Additionally, the adhesion strength decreases by half after the washing process and rougher and more porous textile structures demonstrate better durability. These results are explained by the surface topography of textile materials that define the anchorage areas between the printed layer and the textiles. Originality/value This study is for great importance in the development of smart textiles using FDM process as it presents unique and reliable models used to optimize adhesion resistance of 3D printed PLA primary layer onto PET textiles.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.