An auxetic lattice structure with a negative Poisson's ratio has excellent energy absorption and high fracture toughness.Unlike conventional metamaterials with Poisson's ratio, the auxetic lattice structure has been used in various fields from biomechanics to industrial structural applications to improve mechanical properties.We aim to optimize the design parameters of the auxetic unit cell to minimize the stress concentrations on the surface of the metamaterial based on the analysis of the compressive mechanical behavior of the auxetic lattice structure.After parametrizing the design variables for three types of re-entrant structures, the maximum stress on the structure surface and the Poisson's ratio of the structure was measured through a finite element (FE) parametric study.The results of the FE parametric study were used as training and prediction data to construct an artificial neural network (ANN)-based FE surrogate model.Using the design optimization with a deep neural network (DNN)-based surrogate model, we proposed insights into the design parameters of the auxetic unit cell that minimize the surface stress concentrations.

An auxetic lattice structure with a negative Poisson's ratio has excellent energy absorption and high fracture toughness.Unlike conventional metamaterials with Poisson's ratio, the auxetic lattice structure has been used in various fields from biomechanics to industrial structural applications to improve mechanical properties.We aim to optimize the design parameters of the auxetic unit cell to minimize the stress concentrations on the surface of the metamaterial based on the analysis of the compressive mechanical behavior of the auxetic lattice structure.After parametrizing the design variables for three types of re-entrant structures, the maximum stress on the structure surface and the Poisson's ratio of the structure was measured through a finite element (FE) parametric study.The results of the FE parametric study were used as training and prediction data to construct an artificial neural network (ANN)-based FE surrogate model.Using the design optimization with a deep neural network (DNN)-based surrogate model, we proposed insights into the design parameters of the auxetic unit cell that minimize the surface stress concentrations.

본 논문에서는 나노 탄소 압저항 필라멘트(NCPF: nano-carbon piezoresistive filament)로 제작된 3D 프린팅 센서의 특성을 실험 연구하였다. ABS 수지와 2 wt%의 다중벽 탄소 나노튜브로 제작된 NCPF 의 압저항 특성을 기반으로, 3D 프린터를 활용하여 스트레인 센서와 토크 센서를 제작하였다. 그리드 패턴의 스트레인 센서는 0.12% 범위의 인장과 압축에 대하여 13%의 선형성을 보였으나, 고분자를 기반으로 하는 센서에서 발생하는 히스테리시스 특성도 함께 나타났다. 스포크(spoke) 형상의 토크 센서는 금속 스트레인 게이지 보다는 낮은 감도(0.06 V/N·m) 특성을 보였으나 선형성 측면에서 유사한 특성을 보였다.

The development of a 3D-Printed Load Cell (PLC) was studied using a nanocarbon composite strain sensor (NCSS) and a 3D printing process. The miniature load cell was fabricated using a low-cost LCD-based 3D printer with UV resin. The NCSS composed of 0.5 wt% MWCNT/epoxy was used to create the flexure of PLC. PLC performance was evaluated under a rated load range; its output was equal to the common value of 2 mV/V. The performance was also evaluated after a calibration in terms of non-linearity, repeatability, and hysteresis, with final results of 2.12%, 1.60%, and 4.42%, respectively. Creep and creep recovery were found to be 1.68 (%FS) and 4.16 (%FS). The relative inferiorities of PLC seem to originate from the inherent hyper-elastic characteristics of polymer sensors. The 3D PLC developed may be a promising solution for the OEM/design-in load cell market and may also result in the development of a novel 3D-printed sensor.

For an engineering feasibility study, we studied a simple design to improve NCSS (nanocarbon composite strain sensor) sensitivity by using its geometric pattern at a macro scale. We fabricated bulk- and grid-type sensors with different filler content weights (wt.%) and different sensor lengths and investigated their sensitivity characteristics. We also proposed a unit gauge factor model of NCSS to find a correlation between sensor length and its sensitivity. NCSS sensitivity was improved proportional to its length incremental ratio and we were able to achieve better linear and consistent data from the grid type than the bulk type one. We conclude that the longer sensor length results in a larger change of resistance due to its piezoresistive unit summation and that sensor geometric pattern design is one of the important issues for axial load and deformation measurement.

In this study, a carbon nanotube based flexible strain sensor was experimentally studied to develop a large deformable sensor. The sensor was made of silicone rubber and a multi-wall carbon nanotube as an electrically conductive filler with less than 3 wt% content. Since the nanocomposite sensor has piezoresistivity, its electrical resistivity is changed according to deformation. The sensor was fabricated as a sandwich structure to prevent buckling under large deformation and the electrodes were made with conductive fabrics to be able to connect it on large deformable materials. Flexible strain sensors are less sensitive than commercial strain sensors, but can have sufficient sensing characteristics to be large strain sensors. The output voltage range of the sensor can be enough to cover the large deformation of the sensing object completely due to it lower sensitivity. Further research should develop a high hysteresis compensation algorithm due to viscoelasticity to obtain more accurate rubber parts deformation measure.

Abstract We presented a novel impact paint sensor made of piezoresistive nano-carbon composites and studied its characteristics. The paint sensors were fabricated with multi-walled carbon nanotube (MWCNT), exfoliated graphite nano-platelets (xGnP), and a hybrid type of the two nano-carbon fillers and were sprayed onto a carbon fiber-reinforced plastic panel for lab testing. In ball drop impact test, the MWCNT-xGnP-based hybrid sensor showed the best characteristics in impact energy sensing within the range 0.07–1.0 J. We also studied the piezoresistive mechanism due to dimensional variations of nano carbon isotopes for sensor design. Piezorestivity of nano-carbon sensor was significantly dominated the electrical contact variation of the electrical fillers in a matrix. This study is expected to provide a feasibility test for designing impact paint sensors with optimized sensitivity for a composite structural health monitoring.

본 논문에서는 4차 산업혁명의 핵심인 측정소자 개발을 위한 나노 탄소 스트레인 센서(Nano-Carbon Strain Sensor, NCSS)의 제작 및 특성과 그 응용을 기술하였다. NCSS는 나노 탄소 소재를 기반으로 하는 전왜성 복합소재(Nano Carbon Piezoresistive Composite, NCPC)로 제작된다. 따라서 NCSS는 인쇄전자 방식이나 3D 프린팅 적층가공 기술을 활용하여 제작될 수 있을 뿐만 아니라 혼합 소재의 선택에 따라 유연성을 부여할 수도 있다. 이로 인하여 다양한 재질과 형상의 구조형 센서들이 자유롭게 설계되고 경제적으로 제작 할 수 있으리라 기대된다.