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Coupled Eulerian–Lagrangian prediction of thermal and residual stress environments in dissimilar friction stir welding of aluminum alloys
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The evaluation of residual stresses (RS) induced by the friction stir welding (FSW) process is crucial in anticipating the performance of the welded structure. The existence of such residual stresses within a friction stir welded structure may lead to excessive distortion and weakness to afford the applied external loads. To assess quantitatively the effect of these residual stresses generated by FSW process, the current paper implements a Coupled Eulerian–Lagrangian (CEL) finite element simulation to analyze both thermal and subsequent resulted remaining stress environments in dissimilar friction stir welding of AA6061-T6 and AA2024-T3 alloys. The thermal analysis step was conducted first and followed by a mechanical analysis step in which the residual stresses distribution throughout the whole dissimilar FSWed alloys were captured. To validate the simulation results, K-type thermocouples, in addition to A-type rosette strain gauges, were planted to measure both temperature history and residual stress field generated as a consequence of the thermal environment. The effect of changing FSW working variables like rotation and traverse speeds on both the thermal and residual stress environments was investigated. The obtained results demonstrated that the temperature, as well as the residual stress, was higher in the sample retreating side rather than the proposed advancing side, and a fair correlation between the experimental and simulation results was attained. Quantitatively, the longitudinal residual stress was higher in contrast to the transverse value, and it varied from being tensile in the zone beneath the tool shoulder to compressive away from the welding tool. Furthermore, the plastic strain produced owing to the surface contact between the welding tool and the dissimilar aluminum sample was higher on the retreating side.

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Publication Date
Wed Jan 01 2025
Journal Name
Indian Journal Of Pure And Applied Physics
Mechanical and Thermal Properties of HDPE/LDPE Blends for Industrial Applications
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to evaluate their suitability for industrial applications. Structural, mechanical, thermal, and surface analyses were performed to determine the influence of blend composition. The results showed that the 60/40 (HDPE/LDPE) blend exhibited superior mechanical performance, including tensile strength, impact resistance, and hardness, while the 80/20 blend demonstrated higher thermal stability and morphological uniformity. The results obtained demonstrate the potential for using HDPE/LDPE blends in many areas, including packaging, insulation, and engineering products that require a balance between durability and thermal stability. They also suggest their potential future application in agricultural irrigation pipes, which require highly reliabl

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Publication Date
Fri Apr 01 2022
Journal Name
Journal Of Engineering
Prediction of Shear Strength Parameters of Gypseous Soil using Artificial Neural Networks
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The shear strength of soil is one of the most important soil properties that should be identified before any foundation design. The presence of gypseous soil exacerbates foundation problems. In this research, an approach to forecasting shear strength parameters of gypseous soils based on basic soil properties was created using Artificial Neural Networks. Two models were built to forecast the cohesion and the angle of internal friction. Nine basic soil properties were used as inputs to both models for they were considered to have the most significant impact on soil shear strength, namely: depth, gypsum content, passing sieve no.200, liquid limit, plastic limit, plasticity index, water content, dry unit weight, and initial

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Publication Date
Wed Dec 30 2009
Journal Name
Iraqi Journal Of Chemical And Petroleum Engineering
Prediction of the Point Efficiency of Sieve Tray Using Artificial Neural Network
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An application of neural network technique was introduced in modeling the point efficiency of sieve tray, based on a
data bank of around 33l data points collected from the open literature.Two models proposed,using back-propagation
algorithm, the first model network consists: volumetric liquid flow rate (QL), F foctor for gas (FS), liquid density (pL),
gas density (pg), liquid viscosity (pL), gas viscosity (pg), hole diameter (dH), weir height (hw), pressure (P) and surface
tension between liquid phase and gas phase (o). In the second network, there are six parameters as dimensionless
group: Flowfactor (F), Reynolds number for liquid (ReL), Reynolds number for gas through hole (Reg), ratio of weir
height to hole diqmeter

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Publication Date
Sat Dec 31 2022
Journal Name
Journal Of Life Science And Applied Research
THE ROLE OF OPTIFEED®, VÊO® PREMIUM, AND OLEOBIOTEC® IN DIETS FOR IMPROVEMENT OF THE PRODUCTION PERFORMANCE OF MALE BROILERS IN HEAT STRESS
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The present study was conducted to investigate the effect of adding various levels of Optifeed®, VêO® premium and Oleobiotec® to the diets as appetite stimulants in the production Performance of broiler males under heat stress conditions. The experiment was done for 42 days for the period from 30 August 2018 to 11 of October 2018 at the Poultry Research Station of the Livestock Research Department / Agricultural Research Department / Ministry of Agriculture (Baghdad - Abu Ghraib). In this study, 270 - one-day broiler males (Ross 308) were reared with the mean body weight of 37 g/chick, distributed randomly on 18 pens with dimensions of 2 x 3 m (length x width). The experimental treatments involved six treatments with three repli

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Publication Date
Thu Nov 29 2018
Journal Name
Al-khwarizmi Engineering Journal
Surface Roughness Prediction for Steel 304 In Edm Using Response Graph Modeling
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Electrical Discharge Machining (EDM) is a non-traditional cutting technique for metals removing which is relied upon the basic fact that negligible tool force is produced during the machining process. Also, electrical discharge machining is used in manufacturing very hard materials that are electrically conductive. Regarding the electrical discharge machining procedure, the most significant factor of the cutting parameter is the surface roughness (Ra). Conventional try and error method is time consuming as well as high cost. The purpose of the present research is to develop a mathematical model using response graph modeling (RGM). The impact of various parameters such as (current, pulsation on time and pulsation off time) are studied on

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Publication Date
Mon Dec 30 2024
Journal Name
Iraqi Journal Of Chemical And Petroleum Engineering
Reservoir permeability prediction based artificial intelligence techniques
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   Predicting permeability is a cornerstone of petroleum reservoir engineering, playing a vital role in optimizing hydrocarbon recovery strategies. This paper explores the application of neural networks to predict permeability in oil reservoirs, underscoring their growing importance in addressing traditional prediction challenges. Conventional techniques often struggle with the complexities of subsurface conditions, making innovative approaches essential. Neural networks, with their ability to uncover complicated patterns within large datasets, emerge as a powerful alternative. The Quanti-Elan model was used in this study to combine several well logs for mineral volumes, porosity and water saturation estimation. This model goes be

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Publication Date
Tue Sep 08 2020
Journal Name
Baghdad Science Journal
A Study of Apelin-36 and GST Levels with Their Relationship to Lipid and Other Biochemical Parameters in the Prediction of Heart Diseases in PCOS Women Patients
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This work studies the role of serum apelin-36 and Glutathione S-transferases (GST) activity in association with the hormonal, metabolic profiles and their link to the risk of cardiovascular disease (CVD) in healthy and patients' ladies with polycystic ovary syndrome (PCOS). A total of fifty-four (PCOS) patients and thirty-one healthy woman as a control have been studied. The PCOS patients were subdivided on the basis of body-mass-index (BMI), into 2-subgroups (the first group was obese-PCOS with BMI ≥ 30 and the second group was non-obese PCOS MBI<30). Fasting-insulin-levels and Lipid-profile, Homeostatic-model assessment-of-insulin-resistance (HOMA-IR), follicle-stimulating-hormone (FSH), luteinizing-hormone (LH), testosterone and

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Publication Date
Sun Oct 01 2023
Journal Name
Solid State Communications
Influence of In-dopant on the optoelectronic properties of thermal evaporated CuAlTe2 films
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In the current study, CuAl0.7In0.3Te2 thin films with 400 nm thickness were deposited on glass substrates using thermal evaporation technique. The films were annealed at various annealing temperatures of (473,573,673 and 773) K. Furthermore, the films were characterized by X-ray Diffraction spectroscopy (XRD), field emission scanning electron microscopy (FESEM), atomic force microscopy (AFM), and Ultra violet-visible (UV–vis). XRD patterns confirm that the films exhibit chalcopyrite structure and the predominant diffraction peak is oriented at (112). The grain size and surface roughness of the annealed films have been reported. Optical properties for the synthesized films including, absorbance, transmittance, dielectric constant, and refr

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Publication Date
Sun Oct 01 2023
Journal Name
Solid State Communications
Influence of In-dopant on the optoelectronic properties of thermal evaporated CuAlTe2 films
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Publication Date
Sat Oct 28 2023
Journal Name
Baghdad Science Journal
Effect of Green-biosynthesis Aluminum Nanoparticles (Al NPs) on Salmonella enterica Isolated from Baghdad City
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This study is aimed to Green-synthesize and characterize Al NPs from Clove (Syzygium aromaticum
L.) buds plant extract and to investigate their effect on isolated and characterized Salmonella enterica growth.
S. aromaticum buds aqueous extract was prepared from local market clove, then mixed with Aluminum nitrate
Al(NO3)3. 9 H2O, 99.9% in ¼ ratio for green-synthesizing of Al NPs. Color change was a primary confirmation
of Al NPs biosynthesis. The biosynthesized nanoparticles were identified and characterized by AFM, SEM,
EDX and UV–Visible spectrophotometer. AFM data recorded 122nm particles size and the surface roughness
RMs) of the pure S. aromaticum buds aqueous extract recorded 17.5nm particles s

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