The purpose of this study is to investigate surface treatments and fiber types on adhesion properties polylactic acid (PLA) three-dimensional (3D) parts printed on woven fabrics.
Abstract
Purpose
The purpose of this study is to investigate surface treatments and fiber types on adhesion properties polylactic acid (PLA) three-dimensional (3D) parts printed on woven fabrics.
Design/methodology/approach
The cotton, flax and jute fabrics were exposed to alkali, hydrogen peroxide, stearic acid and ionic liquid treatments to modify surface characteristics before PLA 3D printing. The modification efficiency was assessed with Fourier transform infrared spectroscopy (FTIR) and scanning electron microscope (SEM) analyses. Then, fused deposition modeling (FDM) printer and PLA filament were used for 3D printing onto the untreated and treated fabrics. The adhesion strength between the fabrics and PLA 3D parts were tested according to DIN 53530 via universal tensile tester.
Findings
The fabric structure is effective on adhesion force and greater values were observed for plain weave fabrics. Maximum separation forces were obtained for alkali pretreated fabrics among jute and cotton. Hydrogen peroxide treatment also increased adhesion forces for jute and cotton fabrics while decreasing for flax fabrics. Stearic acid and ionic liquid treatments reduced adhesion forces compared to untreated fabrics. Treatments are effective to alter adhesion via changing surface chemistry, surface morphology and fabric physical properties but display different effects related to fabric material.
Originality/value
This study provides experimental information about effects of different fiber types and surface treatments on adhesion strength of PLA 3D parts. There is limited research about comprehensive observation on 3D printing on cellulosic-woven fabrics.
Yaşar Erayman Yüksel and Yasemin Korkmaz
Durability of textile materials under wet conditions has become very important in recent years. The water repellency performance of fabrics should be maintained in the seam areas…
Abstract
Purpose
Durability of textile materials under wet conditions has become very important in recent years. The water repellency performance of fabrics should be maintained in the seam areas. The purpose of this paper is to analyze the effect of water repellent agents and sewing threads on the seam and water repellency performance properties of woven fabrics.
Design/methodology/approach
100 percent polyester woven fabrics were treated with three different water repellent finishing agents (silicone, fluorocarbons with 6 and 8 carbons) and then sewn with different sewing threads (unfinished/water repellent finished polyester/cotton corespun and polyamide filament). Afterwards, mechanical properties, seam performance and water repellency properties of these materials were measured.
Findings
The effect of finishing which was statistically significant on seam strength only in warp direction was significant on seam elongation and efficiency in both warp and weft directions. Seam strength, seam efficiency, seam slippage and seam pucker of fabrics sewn with polyamide threads were higher than others. The fluorocarbons applied to the fabrics gave higher water repellency values than silicones. In addition, as the chain length increased in fluorocarbons, water repellency performance increased. Sewing process reduced water resistance of fabrics; however, water repellent finish applied to the threads increased water resistance of fabrics.
Originality/value
As a result of the literature review, it was seen that water repellency property of a wear were studied in only seamless areas of fabrics. Originality of this study is that the water repellency properties are also analyzed in the seam areas of the fabrics and evaluated together with the seam performance characteristics.
Details
Keywords
Suhang Yang, Tangrui Chen and Zhifeng Xu
Recycled aggregate self-compacting concrete (RASCC) has the potential for sustainable resource utilization and has been widely applied. Predicting the compressive strength (CS) of…
Abstract
Purpose
Recycled aggregate self-compacting concrete (RASCC) has the potential for sustainable resource utilization and has been widely applied. Predicting the compressive strength (CS) of RASCC is challenging due to its complex composite nature and nonlinear behavior.
Design/methodology/approach
This study comprehensively evaluated commonly used machine learning (ML) techniques, including artificial neural networks (ANN), random trees (RT), bagging and random forests (RF) for predicting the CS of RASCC. The results indicate that RF and ANN models typically have advantages with higher R2 values, lower root mean square error (RMSE), mean square error (MSE) and mean absolute error (MAE) values.
Findings
The combination of ML and Shapley additive explanation (SHAP) interpretable algorithms provides physical rationality, allowing engineers to adjust the proportion based on parameter analysis to predict and design RASCC. The sensitivity analysis of the ML model indicates that ANN’s interpretation ability is weaker than tree-based algorithms (RT, BG and RF). ML regression technology has high accuracy, good interpretability and great potential for predicting the CS of RASCC.
Originality/value
ML regression technology has high accuracy, good interpretability and great potential for predicting the CS of RASCC.