The shape memory textile actuator uses a shape memory alloy that changes its crystal structure according to temperature and then returns to its initial shape, which is suitable for wearable applications that value wearing and portability. The shape memory effect of returning to the initial shape and the complex fabric structure influence each other, and accordingly, various drives have been measured in many studies. Therefore, in this study, the driving force, which is the most important physical property of the shape memory textile actuator, was analyzed through a scale-up finite element analysis. In the wire scale, a shape memory alloy wire was obtained for an analysis of the mechanical and thermal properties through tensile tests and DSC, and in the unit cell scale, a 3D knit structure was modeled using the Texgen and Weftknit programs. Finally, a supercell of size 5 × 5 was subjected to external deformation by displacement and heating conditions using disposition through the ANSYS program. The driving force was measured through microanalysis. Subsequently, the driving force of the manufactured shape memory textile actuator was compared and analyzed to determine the suitability of this method. Furthermore, the direction of subsequent studies was mapped based on the analysis presented on the differences in the maximum driving force and error rate.
Health monitoring sensors that are attached to clothing are a new trend of the times, especially stretchable sensors for human motion measurements or biological markers. However, price, durability, and performance always are major problems to be addressed and three-dimensional (3D) printing combined with conductive flexible materials (thermoplastic polyurethane) can be an optimal solution. Herein, we evaluate the effects of 3D printing-line directions (45°, 90°, 180°) on the sensor performances. Using fused filament fabrication (FDM) technology, the sensors are created with different print styles for specific purposes. We also discuss some main issues of the stretch sensors from Carbon Nanotube/Thermoplastic Polyurethane (CNT/TPU) and FDM. Our sensor achieves outstanding stability (10,000 cycles) and reliability, which are verified through repeated measurements. Its capability is demonstrated in a real application when detecting finger motion by a sensor-integrated into gloves. This paper is expected to bring contribution to the development of flexible conductive materials—based on 3D printing.
Among wearable e-textiles, conductive textile yarns are of particular interest because they can be used as flexible and wearable sensors without affecting the usual properties and comfort of the textiles. Firstly, this study proposed three types of piezoresistive textile sensors, namely, single-layer, double-layer, and quadruple-layer, to be made by the Jacquard processing method. This method enables the programmable design of the sensor’s structure and customizes the sensor’s sensitivity to work more efficiently in personalized applications. Secondly, the sensor range and coefficient of determination showed that the sensor is reliable and suitable for many applications. The dimensions of the proposed sensors are 20 × 20 cm, and the thicknesses are under 0.52 mm. The entire area of the sensor is a pressure-sensitive spot. Thirdly, the effect of layer density on the performance of the sensors showed that the single-layer pressure sensor has a thinner thickness and faster response time than the multilayer pressure sensor. Moreover, the sensors have a quick response time (<50 ms) and small hysteresis. Finally, the hysteresis will increase according to the number of conductive layers. Many tests were carried out, which can provide an excellent knowledge database in the context of large-area piezoresistive textile sensors using manufacturing by Jacquard processing. The effects of the percolation of CNTs, thickness, and sheet resistance on the performance of sensors were investigated. The structural and surface morphology of coating samples and SWCNTs were evaluated by using a scanning electron microscope. The structure of the proposed sensor is expected to be an essential step toward realizing wearable signal sensing for next-generation personalized applications.
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