Milling process is a common machining operation that is used in the manufacturing of complex surfaces. Machining-induced residual stresses (RS) have a great impact on the performance of machined components and the surface quality in face milling operations with parameter cutting. The properties of engineering material as well as structural components, specifically fatigue life, deformation, impact resistance, corrosion resistance, and brittle fracture, can all be significantly influenced by residual stresses. Accordingly, controlling the distribution of residual stresses is indeed important to protect the piece and avoid failure. Most of the previous works inspected the material properties, tool parameters, or cutting parameters, but few of them provided the distribution of RS in a direct and singular way. This work focuses on studying and optimizing the effect of cutting speed, feed rate, and depth of cut for 6061-T3 aluminum alloy on the RS of the surface. The optimum values of geometry parameters have been found by using the L27 orthogonal array. Analysis and simulation of RS by using an artificial neural network (ANN) were carried out to predict the RS behavior due to changing machining process parameters. Using ANN to predict the behavior of RS due to changing machining process parameters is presented as a promising method. The milling process produces more RS at high cutting speed, roughly intermediate feed rate, and deeper cut, according to the results. The best residual stress obtained from ANN is ‒135.204 N/mm2 at a cutting depth of 5 mm, feed rate of 0.25 mm/rev and cutting speed of 1,000 rpm. ANN can be considered a powerful tool for estimating residual stress
Groundwater can be assessed by studying water wells. This study was conducted in Al-Wafa District, Anbar Governorate, Iraq. The water samples were collected from 24 different wells in the study area, in January 2021. A laboratory examination of the samples was conducted. Geographical information systems technique was relied on to determine the values of polluting elements in the wells. The chemical elements that were measured were [cadmium, lead, cobalt and chromium]. The output of this research were planned to be spatial maps that show the distribution of the elements with respect to their concentrations. The results show a variation in the heavy elements concentrations at the studied area groundwater. The samples show different values
... Show MoreA computational investigation is carried out to describe the behaviour of reflected electrons upon a charged insulator sample and producing mirror effect images. A theoretical expression for the scanning electron path equation is derived concerning Rutherford scattering and some electrostatic aspects. The importance of the derived formula come from its correlation among some of the most important parameters that controls the mirror effect phenomena. These parameters, in fact, are the trapped charges, incident angle and the scanning potential which investigated by considering its influences on the incident electrons. A pervious experimental operation requirements are adopted for operating the introduced expression. However, the obtained r
... Show MoreIn this paper, the process for finding an approximate solution of nonlinear three-dimensional (3D) Volterra type integral operator equation (N3D-VIOE) in R3 is introduced. The modelling of the majorant function (MF) with the modified Newton method (MNM) is employed to convert N3D-VIOE to the linear 3D Volterra type integral operator equation (L3D-VIOE). The method of trapezoidal rule (TR) and collocation points are utilized to determine the approximate solution of L3D-VIOE by dealing with the linear form of the algebraic system. The existence of the approximate solution and its uniqueness are proved, and illustrative examples are provided to show the accuracy and efficiency of the model.
Mathematical Subject Classificat
... Show MoreThe purpose of this research is to investigate the impact of corrosive environment (corrosive ferric chloride of 1, 2, 5, 6% wt. at room temperature), immersion period of (48, 72, 96, 120, 144 hours), and surface roughness on pitting corrosion characteristics and use the data to build an artificial neural network and test its ability to predict the depth and intensity of pitting corrosion in a variety of conditions. Pit density and depth were calculated using a pitting corrosion test on carbon steel (C-4130). Pitting corrosion experimental tests were used to develop artificial neural network (ANN) models for predicting pitting corrosion characteristics. It was found that artificial neural network models were shown to be
... Show MoreAbstract: The aim of the current research is to identify (the relationship between deep understanding skills and mathematical modeling among fifth grade students) the research sample consisted of (411) male and female students of the fifth grade of biology distributed over the General Directorates of Education in Baghdad / Al-Rusafa / 2 / and Al-Karkh / 1 /, and two research tools were built: 1- A test of deep understanding skills, consisting of (20) test items and a scale for two skills. 2- The second test consists of (24) test items distributed among (18) essay items and (6) objective items. The psychometric properties of validity, stability, discriminatory strength, and effectiveness of alternatives were verified for the two tests fo
... Show MoreThe pandemic SARS-CoV-2 is highly transmittable with its proliferation among nations. This study aims to design and exploring the efficacy of novel nirmatrelvir derivatives as SARS entry inhibitors by adapting a molecular modeling approach combined with theoretical design. The study focuses on the preparation of these derivatives and understanding their effectiveness, with a special focus on their binding affinity to the S protein, which is pivotal for the virus’s access to the host cell. Considering molecular docking aspects in the scope of a study on nirmatrelvir derivatives and S protein, dynamics simulations with 25 nanoseconds of their binding are explored. The study shows that these derivatives might work as effective antivi
... Show MoreOver the last few decades the mean field approach using selfconsistent
Haretree-Fock (HF) calculations with Skyrme effective
interactions have been found very satisfactory in reproducing
nuclear properties for both stable and unstable nuclei. They are
based on effective energy-density functional, often formulated in
terms of effective density-dependent nucleon–nucleon interactions.
In the present research, the SkM, SkM*, SI, SIII, SIV, T3, SLy4,
Skxs15, Skxs20 and Skxs25 Skyrme parameterizations have been
used within HF method to investigate some static and dynamic
nuclear ground state proprieties of 84-108Mo isotopes. In particular,
the binding energy, proton, neutron, mass and charge densities
Software-defined networking (SDN) is an innovative network paradigm, offering substantial control of network operation through a network’s architecture. SDN is an ideal platform for implementing projects involving distributed applications, security solutions, and decentralized network administration in a multitenant data center environment due to its programmability. As its usage rapidly expands, network security threats are becoming more frequent, leading SDN security to be of significant concern. Machine-learning (ML) techniques for intrusion detection of DDoS attacks in SDN networks utilize standard datasets and fail to cover all classification aspects, resulting in under-coverage of attack diversity. This paper proposes a hybr
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