Carbon-fiber-reinforced polymer (CFRP) is widely acknowledged as a leading advanced material structure, offering superior properties compared to traditional materials, and has found diverse applications in several industrial sectors, such as that of automobiles, aircrafts, and power plants. However, the production of CFRP composites is prone to fabrication problems, leading to structural defects arising from cycling and aging processes. Identifying these defects at an early stage is crucial to prevent service issues that could result in catastrophic failures. Hence, routine inspection and maintenance are crucial to prevent system collapse. To achieve this objective, conventional nondestructive testing (NDT) methods are utilized to inspect CFRP components. However, the restricted field penetration within the CFRP makes conventional NDT approaches ineffective. Recently, microwave techniques have been developed to address the challenges associated with CFRP inspection by providing better material penetration and more precise results. This paper offers a review of the primary NDT methods employed to inspect CFRP composites, emphasizing microwave-based NDT techniques and their key features.
Deep learning techniques are applied in many different industries for a variety of purposes. Deep learning-based item detection from aerial or terrestrial photographs has become a significant research area in recent years. The goal of object detection in computer vision is to anticipate the presence of one or more objects, along with their classes and bounding boxes. The YOLO (You Only Look Once) modern object detector can detect things in real-time with accuracy and speed. A neural network from the YOLO family of computer vision models makes one-time predictions about the locations of bounding rectangles and classification probabilities for an image. In layman's terms, it is a technique for instantly identifying and recognizing
... Show MoreDeep learning techniques are used across a wide range of fields for several applications. In recent years, deep learning-based object detection from aerial or terrestrial photos has gained popularity as a study topic. The goal of object detection in computer vision is to anticipate the presence of one or more objects, along with their classes and bounding boxes. The YOLO (You Only Look Once) modern object detector can detect things in real-time with accuracy and speed. A neural network from the YOLO family of computer vision models makes one-time predictions about the locations of bounding rectangles andclassification probabilities for an image. In layman's terms, it is a technique for instantly identifying and rec
... Show MoreAim: surface modification of titanium using fiber laser 1064 nm to enhance the bond strength to resin cement. Material and Methods: thirty titanium discs of 0.6 cm x 0.3 cm (diameter and thickness respectively) were categorized after preparation into three groups (n=10) as follows: control group with no surface treatment and two test groups were treated with fiber laser after estimation the appropriate parameters in the pilot study which are 81 ns pulse duration, 30,000 Hz frequency, 50 µm spot size and 10,000 mm/s scanning speed and different average power values (10 W and 20 W) depending on the tested group. Titanium discs surface characterization was performed by scanning electron microscope (SEM), a
... Show MoreThe deposition method of perovskite solar cell layers significantly impacts device functionality and the achievement of industrial goals. Aluminum (Al) nanoparticles with rutile titanium oxide (TiO2) nanoparticle thin films are fabricated on Fluorine Tin Oxide (FTO) glass substrates by nanosecond pulsed fiber laser deposition (PLD) to be used as a plasmonic electron transport layer (ETL) in perovskite solar cell (PSC). The effect of various pulsed fiber laser parameters on the structural, optical, and surface morphology on Al/TiO2 films is extensively examined utilizing a variety of measurement techniques; X-ray diffraction (XRD), Ultraviolet–visible (UV–Vis) spectroscopy, Field emission scanning electron microscopy (FE-SEM) and Atomic
... Show MoreMost of drinking water consuming all over the world has been treated at the water treatment plant (WTP) where raw water is abstracted from reservoirs and rivers. The turbidity removal efficiency is very important to supply safe drinking water. This study is focusing on the use of multiple linear regression (MLR) and artificial neural network (ANN) models to predict the turbidity removal efficiency of Al-Wahda WTP in Baghdad city. The measured physico-chemical parameters were used to determine their effect on turbidity removal efficiency in various processes. The suitable formulation of the ANN model is examined throughout many preparations, trials, and steps of evaluation. The predict
The contemporary business environment is witnessing increasing calls for modifications to the traditional cost system, and a trend towards adopting cost management techniques to provide appropriate financial and non-financial information for senior and executive departments, including the Resource Consumption Accounting (RCA) technique in question, which classifies costs into fixed and variable to support the decision-making process. Moreover, (RCA) combines two approaches to cost estimation, the first based on activity-based cost accounting (ABC) and the second on the German cost accounting method (GPK). The research aims to provide a conceptual vision for resource consumption accounting, after Considering it as an accounting te
... Show MorePhotonic Crystal Fiber (PCF) based on the Surface Plasmon Resonance (SPR) effect has been proposed to detect polluted water samples. The sensing characteristics are illustrated using the finite element method. The right hole of the right side of PCF core has been coated with chemically stable gold material to achieve the practical sensing approach. The performance parameter of the proposed sensor is investigated in terms of wavelength sensitivity, amplitude sensitivity, sensor resolution, and linearity of the resonant wavelength with the variation of refractive index of analyte. In the sensing range of 1.33 to 1.3624, maximum sensitivities of 1360.2 nm ∕ RIU and 184 RIU−1 are achieved with the high sensor resolutions of 7
... Show MoreA chemical optical fiber sensor based on surface plasmon resonance (SPR) was developed and implemented using multimode plastic optical fiber. The sensor is used to detect and measure the refractive index and concentration of various chemical materials (Urea, Ammonia, Formaldehyde and Sulfuric acid) as well as to evaluate the performance parameters such as sensitivity, signal to noise ratio, resolution and figure of merit. It was noticed that the value of the sensitivity of the optical fiber-based SPR sensor, with 60nm and 10 mm long, Aluminum(Al) and Gold (Au) metals film exposed sensing region, was 4.4 μm, while the SNR was 0.20, figure of merit was 20 and resolution 0.00045. In this work a multimode
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