Delamination in fiberglass pre-impregnated laminated composites from ultrasonic a-scan signal using artificial intelligence

Impact-induced delamination (IID) in fiberglass pre-impregnated laminated composites (FGLC) is an important failure mode. Besides affected the material strength and structural reliability, this failure mode normally present minor damage on the surface but the internal damage may extensive. Existing detection method using static and dynamic load response have limitations that are considered static based monitoring and require the sensor to be attached to the test specimen surface. This technique is not suitable as the damage caused by the impact normally occurred by accident at random location. Thus, detection and classification of IID using artificial neural network from ultrasonic signal has great potential to be applied, but no attempt has been made to detect and classify this failure mode in FGLC material. The classification of delamination against impact not only applicable as prediction tool to characterise the delamination, it also can be used as reference during inspecting the FGLC under specific conditions. In this study, the potential of using ultrasonic immersion testing for detecting the IID in FGLC type 7781 E-Glass fabric is studied. Several findings and development have been achieved in this study such as the relationship between delamination area and the increasing of an impact energy, where the rate is between 23 to 45 percent. Besides, it was found that the diameter of the impact damage is directly increase with the increasing of the impact energy in the range of 21 until 46 percent while for the impact damage area is between 24 until 42 percent. In addition, the dynamic segmentation algorithm has been successfully developed in this study to automatically segment the A-scan signal with regardless the xxi variation of gap distance between transducer and specimen surface. Based on the ultrasonic inspection result, it was found that the delamination is extend internally up to 35.90 percent and the average percentage different of the measurement result which is taken from DT and NDT is just 4.72 percent and acceptable. Since the achieved classification result is highly accurate, which is exceeded 99.29 percent, it can be concluded that the selected features for the classification input is successful and the use of artificial neural network from ultrasonic A-scan signal has shown its applicability to classify the different type of the impact-induced delamination in FGLC plate

Detection And Classification Of Impact-Induced Delamination In Fiberglass Pre-Impregnated Laminated Composites From Ultrasonic A-Scan Signal Using Artificial Intelligence

Impact-induced delamination (IID) in fiberglass pre-impregnated laminated composites (FGLC) is an important failure mode. Besides affected the material strength and structural reliability, this failure mode normally present minor damage on the surface but the internal damage may extensive. Existing detection method using static and dynamic load response have limitations that are considered static based monitoring and require the sensor to be attached to the test specimen surface. This technique is not suitable as the damage caused by the impact normally occurred by accident at random location. Thus, detection and classification of IID using artificial neural network from ultrasonic signal has great potential to be applied, but no attempt has been made to detect and classify this failure mode in FGLC material. The classification of delamination against impact not only applicable as prediction tool to characterise the delamination, it also can be used as reference during inspecting the FGLC under specific conditions. In this study, the potential of using ultrasonic immersion testing for detecting the IID in FGLC type 7781 E-Glass fabric is studied. Several findings and development have been achieved in this study such as the relationship between delamination area and the increasing of an impact energy, where the rate is between 23 to 45 percent. Besides, it was found that the diameter of the impact damage is directly increase with the increasing of the impact energy in the range of 21 until 46 percent while for the impact damage area is between 24 until 42 percent. In addition, the dynamic segmentation algorithm has been successfully developed in this study to automatically segment the A-scan signal with regardless the variation of gap distance between transducer and specimen surface. Based on the ultrasonic inspection result, it was found that the delamination is extend internally up to 35.90 percent and the average percentage different of the measurement result which is taken from DT and NDT is just 4.72 percent and acceptable. Since the achieved classification result is highly accurate, which is exceeded 99.29 percent, it can be concluded that the selected features for the classification input is successful and the use of artificial neural network from ultrasonic A-scan signal has shown its applicability to classify the different type of the impact-induced delamination in FGLC plates

Corrosion Behavior of Steel Sleeve Joint in Different Concentration of Sodium Chloride (NaCl)

A steel sleeve joint is a common way to fix a pipeline that has corroded, however even this type of repair is not immune to corrosion. To understand and increase the effectiveness of the steel sleeve joint, the corrosion behavior of the steel sleeve joint in various sodium chloride concentrations has been examined in this study. Carbon steel sleeve joints in weld and pipe areas were examined using a Scanning Electron Microscope (SEM) and Energy Dispersive Spectroscopy (EDS), which were utilized to determine the surface morphologies and element composition. The sample's parameters include sodium chloride concentrations of 15% and 33% and immersion times of 24, 48, and 72 hours. A Vickers hardness test is also done on the steel sleeve joint sample to compare the hardness of the weld and the pipe part. Results showed that the sample submerged in a 15% sodium chloride concentration corroded by more than 33%, and the corrosion product form increased with immersion time. The Vickers hardness number ranged from 216.4 to 284.8 HV for welds and 132.8 to 182.1 HV for pipe areas, respectively. The different values prove different corrosion behavior happen on both areas of the sample when compared by the results of surface morphologies and the Vickers hardness test

Evaluation System on Haemodynamic Parameters for Stented Carotid Artery: Stent Pictorial Selection Method

Stent implantation is an alternative invasive technique for treating the narrowed artery or stenosis in carotid artery to restore blood to the brain. However, the restenosis process is usually observed after a few weeks of carotid angioplasty and stenting due to abnormal progression of atherosclerosis and thrombosis. Many studies reported that the activity of atherosclerosis and thrombosis is majorly influenced by the geometrical strut configuration. Thus, this study was carried out to determine the haemodynamic performance on different geometrical stent strut configurations based on numerical modelling and statistical analyses. Six different stent strut configurations were 3-D modelled and simulated in different physiological conditions; normal blood pressure (NBP), pre-hypertension (PH) and hypertension stage one (HS1) through computational fluid dynamic (CFD) method. The haemodynamic performance of stent was analysed based on parameters namely time averaged wall shear stress (TAWSS), time averaged wall shear stress gradient (TAWSSG), oscillatory shear index (OSI), relative residence time (RRT) and flow separation parameter (FSP). Meanwhile, Pictorial Selection Method was used to evaluate the best haemodynamic stent performance based on a scoring system. From observation, stent Type II was seen to show the highest score for TAWSS, which was 3.44 regardless of any physiological conditions. For TAWSSG, the lowest score was observed for Type V stent with 0.36. Furthermore, Type VI stent displayed the highest score for OSI while Type IV has the lowest score for FSP with 3.09 and 1.23, respectively. On the other hand, RRT was seen varying according to the physiological condition where the highest score in NBP condition was achieved by Type I while PH and HS1 condition was achieved by Type VI. In conclusion, Type VI has the best stent performance, whereas Type IV has the worst stent performance regarding the scoring system based on haemodynamic parameters. Further, Type I, Type II, Type III and Type V stents showed moderate hemodynamic performances for all physiological conditions