Structural Health Monitoring in Composite Structures: A Comprehensive Review.

This study presents a comprehensive review of the history of research and development of different damage-detection methods in the realm of composite structures. Different fields of engineering, such as mechanical, architectural, civil, and aerospace engineering, benefit excellent mechanical properties of composite materials. Due to their heterogeneous nature, composite materials can suffer from several complex nonlinear damage modes, including impact damage, delamination, matrix crack, fiber breakage, and voids. Therefore, early damage detection of composite structures can help avoid catastrophic events and tragic consequences, such as airplane crashes, further demanding the development of robust structural health monitoring (SHM) algorithms. This study first reviews different non-destructive damage testing techniques, then investigates vibration-based damage-detection methods along with their respective pros and cons, and concludes with a thorough discussion of a nonlinear hybrid method termed the Vibro-Acoustic Modulation technique. Advanced signal processing, machine learning, and deep learning have been widely employed for solving damage-detection problems of composite structures. Therefore, all of these methods have been fully studied. Considering the wide use of a new generation of smart composites in different applications, a section is dedicated to these materials. At the end of this paper, some final remarks and suggestions for future work are presented

An Automatic Detection Method of Nanocomposite Film Element Based on GLCM and Adaboost M1

An automatic detection model adopting pattern recognition technology is proposed in this paper; it can realize the measurement to the element of nanocomposite film. The features of gray level cooccurrence matrix (GLCM) can be extracted from different types of surface morphology images of film; after that, the dimension reduction of film can be handled by principal component analysis (PCA). So it is possible to identify the element of film according to the Adaboost M1 algorithm of a strong classifier with ten decision tree classifiers. The experimental result shows that this model is superior to the ones of SVM (support vector machine), NN and BayesNet. The method proposed can be widely applied to the automatic detection of not only nanocomposite film element but also other nanocomposite material elements

A Novel Image Segmentation Approach for Microstructure Modelling

Microstructure models are used to investigate bulk properties of a material given images of it’s microstructure. Through their use the effect of microstructural features can be investigated independently. Processes can then be optimised to give the desired selection of microstructural features. Currently automatic methods of segmenting SEM images either miss cracks leading to large overestimates of properties such as thermal conductivity or use unjustifiable methods to select a threshold point which class cracks as porosity leading to over estimates of porosity. In this work a novel automatic image segmentation method is presented which produces maps for each phase in the microstructure and an additional phase of cracks. The selection of threshold points is based on the assumption that the brightness values for each phase should be normally distributed. Additional image processing is used to ensure results remain physically relevant. The image segmentation method has been compared to other available methods and shown to be as or more repeatable with changes of brightness and contrast of the input image than relevant alternatives. The resulting modelling route is able to predict density and specific heat to within experimental error, while the expected under predictions for thermal conductivity are observed

Skin thickness as a potential indirect trait for genetic improvement of lamb survival : a thesis presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Animal Science at Massey University, Palmerston North, New Zealand

Lamb survival, as a trait of great economic importance with low heritability, might show more response to indirect selection for traits of higher heritability that are genetically correlated with lamb survival, as a trait of high economic importance. The main objective of this thesis was to explore if ultrasonographically measured skin thickness (ST) at about nine months of age could be considered as an alternative to direct selection for lamb survival from birth to weaning (SBW). For this purpose, in the first step, the reliability of ultrasonography as an accurate and noninvasive method for measurement of ST was validated using plicometry and histometry. In the second experiment, the heritability of ultrasonographically measured ST at an age of about 9 months was estimated to be 0.21 ± 0.03 and 0.20 ± 0.03, respectively from analyses with and without adjustment for live weight at scanning (LWS), implying that the trait would respond to genetic selection. Estimates of genetic correlation of ST with SBW from the analyses with LWS considered as a covariate for ST ranged from 0.16 to 0.35 depending on the minimum number of progeny per sire for each trait, while the corresponding estimates from the analyses with LWS excluded ranged from 0.08 to 0.27. When correction was made for LWS, ST showed genetic correlations of 0.21 ± 0.07, -0.13 ± 0.09, -0.32 ± 0.12, -0.23 ± 0.09, -0.10 ± 0.10, 0.02 ± 0.11, and 0.20 ± 0.11 with fat depth (FD), eye muscle depth (EMD), weights at weaning (WWT), 8 months (LW8), scanning (LWS), and 12 months (LW12), and fleece weight at 12 months (FW12), respectively. The corresponding estimates when no adjustment was made for LWS, were respectively 0.24 ± 0.08, -0.08 ± 0.10, -0.01 ± 0.12, 0.09 ± 0.09, 0.19 ± 0.09, 0.30 ± 0.10, and 0.20 ± 0.11. In the third experiment, the role of skin thickness in thermoregulation through its effect on surface heat loss and a few other indices of cold resistance was explored in two groups of new-born lambs with the thickest skin (thick-skinned category) and the thinnest skin (thin-skinned category) exposed to cold-stress. As a result of lower skin surface temperature (as an indicator of heat loss) in thick-skinned lambs compared to thin-skinned lambs, the first group had higher rectal temperature and were more likely to maintain body temperature during cold stress, even though they produced significantly less heat (W Kg-1). This means there is less need to consume body reserves as a source of energy and consequently better conservation of body reserves in the thick-skinned lambs. In the fourth experiment, skin thickness measured at six to eight months of age was revealed to be a moderately reliable indicator of skin thickness at birth. This is of high importance from both practical and economic points of views. Measuring skin thickness at six to eight months of age is much easier than at birth for sheep farmers/breeders. Furthermore, ultrasound measurement of skin thickness at these ages facilitates simultaneous recording of other traits of importance like fat depth and eye muscle depth, which are normally taken at these ages. In the final study, the effects of genetic variation in the uncoupling protein 1 (UCP1), prolactin (PRL), and prolactin receptor (PRLR) genes on the indices of cold resistance were tested in new-born lambs exposed to cold stress. Although significant effects on some of the indices were observed at/during some time-points/periods of the cold stress, they seem to be mostly due to biases resulting from low number of lambs rather than being real. Considering all the findings, it could be generally concluded that ultrasonographically measured skin thickness at about nine months of age could be considered as a supplement to direct selection for lamb survival in genetic improvement programs. Nevertheless, the large standard errors of the correlations of ST with SBW as well as the unfavorable correlation of ST with other traits should also be considered

On the Processing of Highly Nonlinear Solitarywaves and Guided Ultrasonic Waves for Structural Health Monitoring and Nondestructive Evaluation

The in-situ measurement of thermal stress in civil and mechanical structures may prevent structural anomalies such as unexpected buckling. In the first half of the dissertation, we present a study where highly nonlinear solitary waves (HNSWs) were utilized to measure axial stress in slender beams. HNSWs are compact non-dispersive waves that can form and travel in nonlinear systems such as one-dimensional chains of particles. The effect of the axial stress acting in a beam on the propagation of HNSWs was studied. We found that certain features of the solitary waves enable the measurement of the stress. In general, most guided ultrasonic waves (GUWs)-based health monitoring approaches for structural waveguides are based on the comparison of testing data to baseline data. In the second half of the dissertation, we present a study where some baseline-free signal processing algorithms were presented and applied to numerical and experimental data for the structural health monitoring (SHM) of underwater or dry structures. The algorithms are based on one or more of the following: continuous wavelet transform, empirical mode decomposition, Hilbert transform, competitive optimization algorithm, probabilistic methods. Moreover, experimental data were also processed to extract some features from the time, frequency, and joint timefrequency domains. These features were then fed to a supervised learning algorithm based on artificial neural networks to classify the types of defect. The methods were validated using the numerical model of a plate and a pipe, and the experimental study of a plate in water. In experiment, the propagation of ultrasonic waves was induced by means of laser pulses or transducer and detected with an array of immersion transducers. The results demonstrated that the algorithms are effective, robust against noise, and able to localize and classify the damage

Clinical Research on Diabetic Complications

Refrigeration, air conditioning, and heat pumps (RACHP) have an important impact on the final energy uses of many sectors of modern society, such as residential, commercial, industrial, transport, and automotive. Moreover, RACHP also have an important environmental impact due to the working fluids that deplete the stratospheric ozone layer, which are being phased out according to the Montreal Protocol (1989). Last, but not least, high global working potential (GWP), working fluids (directly), and energy consumption (indirectly) are responsible for a non-negligible quota of greenhouse gas (GHG) emissions in the atmosphere, thus impacting climate change

Pattern Recognition

Pattern recognition is a very wide research field. It involves factors as diverse as sensors, feature extraction, pattern classification, decision fusion, applications and others. The signals processed are commonly one, two or three dimensional, the processing is done in real- time or takes hours and days, some systems look for one narrow object class, others search huge databases for entries with at least a small amount of similarity. No single person can claim expertise across the whole field, which develops rapidly, updates its paradigms and comprehends several philosophical approaches. This book reflects this diversity by presenting a selection of recent developments within the area of pattern recognition and related fields. It covers theoretical advances in classification and feature extraction as well as application-oriented works. Authors of these 25 works present and advocate recent achievements of their research related to the field of pattern recognition

Semi-automatic liquid filling system using NodeMCU as an integrated Iot Learning tool

Computer programming and IoT are the key skills required in Industrial Revolution 4.0 (IR4.0). The industry demand is very high and therefore related students in this field should grasp adequate knowledge and skill in college or university prior to employment. However, learning technology related subject without applying it to an actual hardware can pose difficulty to relate the theoretical knowledge to problems in real application. It is proven that learning through hands-on activities is more effective and promotes deeper understanding of the subject matter (He et al. in Integrating Internet of Things (IoT) into STEM undergraduate education: Case study of a modern technology infused courseware for embedded system course. Erie, PA, USA, pp 1–9 (2016)). Thus, to fulfill the learning requirement, an integrated learning tool that combines learning of computer programming and IoT control for an industrial liquid filling system model is developed and tested. The integrated learning tool uses NodeMCU, Blynk app and smartphone to enable the IoT application. The system set-up is pre-designed for semi-automation liquid filling process to enhance hands-on learning experience but can be easily programmed for full automation. Overall, it is a user and cost friendly learning tool that can be developed by academic staff to aid learning of IoT and computer programming in related education levels and field