DIYALA JOURNAL OF ENGINEERING SCIENCES

The development of three-dimensional printing technology (3D printing) has revolutionized the dental industry by providing a rapid, dependable, and affordable way to create a variety of dental products, including denture bases. This review article presents in-depth research on polymeric materials and their effect on different properties and aspects of dentures manufactured by 3D printing processes. This study indicated that Poly (methylmethacrylate) (PMMA) is a popular material in the 3D printing process of dentures. Despite its widespread use in dentures, more research is required to overcome some disadvantages like brittleness and poor mechanical qualities by utilizing additives to this material for improvement. The polyether ether ketone (PEEK) has remarkable mechanical and thermal properties and is perfect for dentures. A variety of medical and dental applications can benefit from Acrylonitrile butadiene styrene (ABS) toughness and chemical resistance, our review investigation uncovered several potential applications of ABS for printed dentures. Polylactic acid (PLA) is a biocompatible and biodegradable polymer derived from renewable resources and it has been used in dentures. However, additional study is required to enhance its performance in dentures. Furthermore, it was found that the most appropriate 3D printing technology for denture printing is the vat photopolymerization process. Advances in material qualities are assisting in the durable and biocompatible dental prostheses that meet the evolving needs of patients and doctors. Furthermore, in-depth evaluations of environmental sustainability and biocompatibility are essential for advancing the discipline ethically and responsibly.

The current work offers a comparative study that examined the effects of various process parameters, such as dielectric fluid, current (IP), pulse on time (TON), and different conductive powder particles mixed dielectric fluids, on electrical discharge machining (EDM) of AISI 1040, EN31, and HCHCr steels, respectively. The findings indicate that adding conductive particles to the dielectric medium during the powder-mixed EDM (PMEDM) process enhances energy distribution across the spark gap, thereby improving material removal capacity and the surface characteristics of the machined surfaces. Experimental results show that the concentration of powder particles has the most significant impact on surface roughness (Ra) and tool wear rate (TWR), while the most critical factor affecting the material removal rate (MRR) is the current (IP). Additionally, increasing the IP and TON leads to the formation of continuous, thick cracks and a thin white coating on the EDMed surface, as evidenced by scanning electron microscopy (SEM) images of the surface morphology. The study also employs a multi-optimization technique using the Fuzzy-based TOPSIS method to investigate the cumulative effects of the control parameters on performance indicators, namely Ra, MRR, and TWR. In experimental run 8 i.e. moderate IP (5 A), higher TON (180 µs), and higher concentration of copper powder (10 g/l) mixed in EDM oil while machining of AISI 1040, the optimal results i.e. Ra is 5.983 µm, MRR is 27.243 mm3/min, and TWR is 0.775 mm3/min were obtained, respectively.

Two widely used techniques for assessing the efficiency of Steam power plants are energy-exergy assessments. Exergy analysis has lately increased in importance for comprehending mechanism and process control. With today's strict rules about energy security, conservation, and reclamation through several methods, the aforementioned strategy is detrimental. An integrated Steam power plant powered by crude oil, with a single unit processing 200 MW, is the subject of the present study. Based on the energy analysis, the installed condenser accounts for about 56.16% of the total energy loss, making it the primary piece of equipment responsible for energy destruction. The boiler, which is the main piece of equipment in the entire facility, has an exergy destruction of 84.14% according to the analyzed exergy analysis. when the outside temperature rises from 20 to 45 °C. Additionally, CO2 and SO2 emissions decreased by 0.79% and 0.75%, respectively.

The emergence of Fifth Generation (5G) technology has marked a significant advancement in communication networks offering unique speed, capacity, and reliability. However, the successful deployment of 5G networks in urban environments requires careful consideration of various factors, including frequency band selection and infrastructure planning. Since Iraq has not yet witnessed the 5G network implementation, studying how the frequency bands vary and affect performance will provide insights into a smoother migration toward the next-generation cellular systems. This research aims to investigate the feasibility and potential benefits of deploying 5G wireless networks in Basra City utilizing a diverse range of frequency bands at 1.8, 2.1, and 3.6 GHz. A cellular network was designed and analysed to identify the most suitable frequency bands for 5G deployment in this Iraqi city as a case study which can be then generalized for other terrain-like regions. The site locations, antenna heights, transmission power, and antenna down-tilt degree were optimized through three design scenarios. Furthermore, four measurement themes were adopted to evaluate network performance as coverages, power levels, Carrier-to-Interference (C/I), and power density measurement themes. The results showed that despite the network with 3.6 GHz band slightly recorded lower levels of power density, the 1.8 GHz band proved its outperformance compared with the other two bands in terms of coverage, capacity, and C/I. Besides, the 1.8 GHz based design served almost 100 km2 target area with excellent coverage. Consequently, the lower frequency 5G bands seemed to better accommodate users’ demands in Basra City with the outlined constraints.

The motor imagery (MI) electroencephalographic (EEG) signals are leveraged as control commands to numerus engineering applications, such as operating a wheelchair through thought alone. EEG signals are characterized by their non-stationary nature and high dimensionality, posing noteworthy challenges for building robust identification models with limited data samples. The renewed deep convolutional neural network (CNN) has been harnessed in various fields due to its ability to autonomously extract and select latent features addressing problems across various types of EEG signals. However, CNN architectures comprise a vast parameter and hyperparameter space. Normally, parameters are optimized via the back-propagation algorithm, while hyperparameters are adjusted through a lengthy trial-and-error process. This work mitigates the aforementioned problems by employing the genetic algorithm (GA) as an artificial intelligence optimization tool for optimizing crucial architectural hyperparameters including the number of convolution filters, size of filters, and dropout probability. Furthermore, the two parameters of cropping augmentation, used for boosting the EEG training samples, are also optimized by the GA. The proposed model in this paper (GACNN) that includes dual optimization, achieved both by the back-propagation algorithm and GA, has attained promising results in the analysis of MI EEG signals. Experiments are performed on the known brain-computer interface competition IV 2a dataset (BCIC IV 2a). GACNN has recorded an increment of about 17% in Cohen’s kappa coefficient and a decrement of about 30% in standard deviation (SD) in comparison to state-of-the-art studies.

This paper examines the impact of transverse reinforcement and shear span on the shear capacity of reinforced concrete (RC) cantilever beams through both experimental and numerical investigations. The experimental program included testing nine RC beams, each with dimensions of 200 × 300 × 1200 mm. The experimental results were compared with analytical predictions derived from empirical models based on the ACI 318-19 and British Standards (BS) codes.The findings reveal that stirrups significantly enhance shear strength, resulting in an increase in load-carrying capacity ranging from 16.6% to 32.7%, while ductility, as evidenced by increased rotation and curvature, improved by up to 260%. The stirrup spacings employed in the specimens were 75, 100, and 150 mm, with both reinforced and unreinforced specimens exhibiting shear failure.Increasing the shear span-to-depth ratio (a/d) from 2.44 to 3, while keeping the stirrup spacing at 75 mm, resulted in a 12.9% reduction in ultimate load capacity. When the stirrup spacing was increased to 100 mm, the ultimate load capacity experienced a further decline of 23.9%. All beams were analysed using the finite element software ABAQUS, with the finite element analysis (FEA) results closely aligning with the experimental outcomes, particularly in the load-deflection relationship and maximum load capacity. On average, the predicted ultimate load capacity from ABAQUS was 2.7% lower than the experimental results, while the average difference in deflection at ultimate loads between the experimental and numerical results was 7.54%.

Reducing the ratio of box girders' span to their height reduces bending moments and makes them deep members that are controlled by shear behavior. The presence of horizontal curvature generates torsional moments and different deflection between the outer and inner web as a result of twisting. Two experimental specimens with the same dimensions were cast and tested, one straight and the other horizontally curved. The two specimens were numerically modeled using the ABAQUS software for the purpose of verifying the numerical model to study more parameters. The finite element analysis showed a good agreement with the experimental values with a difference of (98-99) %, (94-97)% and 103% of the experiment for ultimate loading, deflection and twisting angle, respectively. It also showed stress paths that match the experimental and theoretical results that explain the behavior of deep beams, such as the Strut and Tie method (STM). The effect of compressive strength, whole width, and the width of the bearing and supporting plates was studied. Results showed that increasing the concrete's compressive strength by about 40-120%, increased the load capacity and decreased its deflection by approximately 6-14% and 3-15%, respectively. The deep box section torsional resistance increased when its width was increased by approximately 17–50%, although this had no significantly affect on the load capacity level. A reduction of 17-33% in the width of the loading and supporting bearing plates caused a 6-16% drop in load capacity besides a notable decrease in stiffness.

The current work consists of a theoretical and experimental investigation of the impact of mass flow rate on the solidification process of phase change materials (PCM) for various inner tube geometries of heat exchangers. The heat exchanger consists of an outer shell filled with paraffin wax as a PCM. Simultaneously, three tested inner tube shapes with the same cross-sectional area are circular, elliptical with minor axis bending, and elliptical with major axis bending. Water was used as the heat transfer fluid (HTF) at two and four L/min flow rates inside the inner tube. A three-dimensional ANSYS simulation was constructed to examine the thermal behaviour of heat exchangers incorporating PCM. The findings demonstrated that when the mass flow rate of HTF decreased, so the solidification time increased. Furthermore, compared to other tube forms, circular tubes offer longer-lasting heat absorption from phase shift materials through the heat transfer fluid. Also, the results show that the heat transfer process between PCM and HTF is controlled by natural convection. solidification begins near the inner tube and then moves towards the casing (horizontal axis at 0°, then inclined axis at 45°, followed by the vertical axis at 90°). Moreover, it was observed that the maximum theoretical and experimental thermal efficiencies recorded were (67.7%, 71.6%,) for the circular inner tube followed by (49.2%, 53.2%) for the elliptical inner tube with minor axis bending and (44.6%, 48.1%) for the elliptical inner tube with major axis bending at a water volumetric flow rate of 2 L/min, respectively.

The increasing demand for accurate and efficient Global Navigation Satellite System (GNSS) receivers, especially in smartphones, has driven the need to combine multi-GNSS signals. Current receivers, based on Global Positioning System (GPS) and Global Navigation Satellite System (GLONASS) signals, often process these signals side-by-side, increasing complexity, size, and power consumption. Meanwhile, the next generation of smartphones and navigation applications will involve Galileo receivers to improve positioning accuracy, faster time to first fix, and reduce the multipath effect, especially in urban canyons or dense forests. Therefore, to avoid the drawbacks of side-by-side implementation; i.e. minimize the large size, and preserve processing time with low power consumption, this work presents a novel approach to simultaneously acquire GPS L1 and Galileo E1 signals in a single processing chain. This achieved by taking advantage of both GPS and Galileo signals are sharing the same carrier frequency and chipping rate. However, our solution requires just a single pre-processing step to overcome the subcarrier effect and one extra multiplication for the Galileo code. The results based on Monte Carlo simulation show that the combining solution; firstly, maintains the acquisition performance for each signal and has the same performance as acquiring each signal alone. Secondly, offers about 48% reduction in the implementation/computational complexity. Eventually, the solution preserves the reliability of the acquisition process by comparing the ratio of the highest peak to the average value in the correlation process, where the results illustrate that both GPS and Galileo signals have the same ratio tendency.

Wireless Sensor Networks (WSNs) provide infrastructure for Various environments and appli- cations that require these sensors must operate in, leading to many challenges, including energy. An Energy-Efficient Edge Routing Protocol (EEERP) has been presented in this paper, the proposed protocol addresses the critical challenge of energy efficiency by leveraging edge sensor nodes(ESNs) strategically positioned at the borders of clusters to act as gateways. The proposed protocol increases the efficiency of whole network by introduce ESNs that facilitate efficient data transmission between sensor nodes and the Base Station(BS). The process of node selection take into consideration in EEERP, where the node with low energy level automatically assigned new ESN, to ensuring continuous network operation and optimal performance. Analysis of the EEERP results validate through MATLAB simulation to compareing it against established protocols such as the performance efficient data aggregation protocol(PEDAP) and high quality of service routing algorithm(HQRA). The results show that EEERP significantly enhances energy efficiency, network stability, and overall operational lifespan. The reliability of the WSN increases as demonstrated in results based dynamic clustering with gateways at cluster border-based ESNs.

The detrimental impact of corrosion on industrial metals, especially mild steel under acidic conditions, underscores the need for the development of effective corrosion inhibitors. In the present work, a new anticorrosion chemical named 2-(2-anthracen-9-yl)-3-chloro-4-oxoazetidin-1-y1)-1- methyl-1H-imidazol-4(5H)-one (AOMI) was synthesized, identified, and assessed for the protection of low-carbon steel corrosion in one molar hydrochloric acid solution. Experimental procedures included weight loss, electrochemical, surface morphological, FTIR, 1H NMR, and hardness measurements, while computational analyses involved quantum chemical calculations. The investigation revealed that AOMI significantly reduced corrosion rates with maximum inhibition efficiencies of 90% at 80 ppm and 60 oC. A spontaneous monolayer was formed on the metal surface. The adsorption of this layer was according to the Langmuir isotherm. SEM and AFM images showed the presence of the protective layer. The experimental results were validated by computational simulations, which showed that bonds were formed with the mild steel surface.

High-data-rate services and applications are evolving continuously, leading to an exponential demand for new and efficient wireless communication systems. The development of next-generation 6G mobile communication technology aims to meet these performance requirements, with the main challenges being high capacity, massive connectivity, and low latency. One prospective solution is to use Free Space Optical (FSO) communication as a backhaul for 6G networks, providing high-capacity transmission without the requirement for physical cables. However, FSO links are highly vulnerable to environmental factors such as rain, fog, and dust storms, which can degrade signal quality and limit the transmission distance. In this paper, the design and simulation of a high-capacity free space optical (FSO) link for 6G backhaul applications in the presence of dust storms, considering the Iraqi environment, were investigated. The performance of the FSO link, operating at a data rate of 100 Gbps, was evaluated using Optisystem software. This evaluation was based on real visibility data related to dust storms collected from northern, central, and southern Iraq during 2008–2022. Dust storms significantly degrade the FSO signal, thereby reducing link distance under low visibility conditions. By incorporating wavelength division multiplexing (WDM) and high-gain Erbium-Doped Fiber Amplifier (EDFA) optical amplifiers, the FSO link achieved a maximum transmission distance of 266 meters under the worst visibility condition (V = 100 meters). This study highlights the viability of FSO technology for 6G networks in regions prone to frequent dust storms.