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Neural Network Modeling of Cutting Force and Chip Thickness Ratio for Turning Aluminum Alloy 7075-T6
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The turning process has various factors, which affecting machinability and should be investigated. These are surface roughness, tool life, power consumption, cutting temperature, machining force components, tool wear, and chip thickness ratio. These factors made the process nonlinear and complicated. This work aims to build neural network models to correlate the cutting parameters, namely cutting speed, depth of cut and feed rate, to the machining force and chip thickness ratio. The turning process was performed on high strength aluminum alloy 7075-T6. Three radial basis neural networks are constructed for cutting force, passive force, and feed force. In addition, a radial basis network is constructed to model the chip thickness ratio. The inputs to all networks are cutting speed, depth of cut, and feed rate. All networks performances (outputs) for all machining force components (cutting force, passive force and feed force) showed perfect match with the experimental data and the calculated correlation coefficients were equal to one. The built network for the chip thickness ratio is giving correlation coefficient equal one too, when its output compared with the experimental results. These networks (models) are used to optimize the cutting parameters that produce the lowest machining force and chip thickness ratio. The models showed that the optimum machining force was (240.46 N) which can be produced when the cutting speed (683 m/min), depth of cut (3.18 mm) and feed rate (0.27 mm/rev). The proposed network for the chip thickness ratio showed that the minimum chip thickness is (1.21), which is at cutting speed (683 m/min), depth of cut (3.18 mm) and feed rate (0.17 mm/rev).

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Publication Date
Fri May 01 2020
Journal Name
International Journal Of Advanced Science And Technology
Improved Merging Multi Convolutional Neural Networks Framework of Image Indexing and Retrieval
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Background/Objectives: The purpose of current research aims to a modified image representation framework for Content-Based Image Retrieval (CBIR) through gray scale input image, Zernike Moments (ZMs) properties, Local Binary Pattern (LBP), Y Color Space, Slantlet Transform (SLT), and Discrete Wavelet Transform (DWT). Methods/Statistical analysis: This study surveyed and analysed three standard datasets WANG V1.0, WANG V2.0, and Caltech 101. The features an image of objects in this sets that belong to 101 classes-with approximately 40-800 images for every category. The suggested infrastructure within the study seeks to present a description and operationalization of the CBIR system through automated attribute extraction system premised on CN

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Publication Date
Tue Dec 31 2013
Journal Name
Al-khwarizmi Engineering Journal
Design of an Adaptive PID Neural Controller for Continuous Stirred Tank Reactor based on Particle Swarm Optimization
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 A particle swarm optimization algorithm and neural network like self-tuning PID controller for CSTR system is presented. The scheme of the discrete-time PID control structure is based on neural network and tuned the parameters of the PID controller by using a particle swarm optimization PSO technique as a simple and fast training algorithm. The proposed method has advantage that it is not necessary to use a combined structure of identification and decision because it used PSO. Simulation results show the effectiveness of the proposed adaptive PID neural control algorithm in terms of minimum tracking error and smoothness control signal obtained for non-linear dynamical CSTR system.

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Publication Date
Sun Feb 01 2026
Journal Name
Journal Of Molecular Structure
Biosynthesis, characterization, antimicrobial and adsorbent effects of γ-aluminum oxide-NPs using aqueous extract of boswellia carterii resin
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γ-Al2O3–NPs were synthesized by a green synthesis process based on Boswellia carterii resin extract and aluminum sulphate in an alkaline medium. Boswellia carterii resin extract is a significant reducing and stabilizing agent for synthesizing γ-Al2O3–NPs.Several techniques, including Fourier–transform infrared (FT-IR), UV–visible spectroscopy, x-ray diffraction, electron microscopy (XRD), energy dispersive x-ray (EDX), scanning electron microscopy (SEM), Transmission electron microscopy (TEM), and atomic force microscopy (AFM), were utilized to investigate the final product. XRD and SEM confirmed a plate-like crystalline structure with an average size of 17.5 nm. FT-IR analysis identified aluminum oxide stretching vibrations (655,

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Publication Date
Tue Jan 01 2019
Journal Name
Journal Of Engineering And Applied Sciences
Effect Of Aluminum On The Structural, Optical, Electrical And Photovoltaic Properties Of ZnSe/n-Si Heterojunction Solar Cell
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Aluminum doped zinc selenide ZnSe/n-Si thin films of (250∓20 nm) thickness with (0.01, 0.02 and 0.03), are depositing on the two type of substrate (glass and n-Si) to manufacture (ZnSe/n-Si) solar cell through using thermal vacuum evaporation procedure. physical and optoelectronic properties were examined for the samples. X-Ray and AFM techniques are using to study the structure properties. The energy band gap of as-deposited ZnSe thin films for changed dopant ratio were ranging from (2.6-2.68 eV). The results of Hall effect show that pure and doping films were (p-type), and the concentration carriers and the carriers mobility increases with increase Al-dopant ratio. The (C-V) have shown that the heterojunction were of abrupt type. In add

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Publication Date
Tue Jun 01 2021
Journal Name
Journal Of Advanced Joining Processes
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 whi

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Publication Date
Mon Dec 30 2013
Journal Name
Iraqi Journal Of Chemical And Petroleum Engineering
Corrosion Inhibition of Galvanic Couple Copper Alloy/Mild Steel in Cooling Water System
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The driving idea for the present work was to combine the effect of polyvinyl alcohol (PVA) as corrosion inhibitor with the distance between the anodic and cathodic elements of the galvanic cell, beside their area ratio, in scope of synergistic suppression of galvanic corrosion on Cu/Fe model couple, using weight loss method. The performance affecting galvanic corrosion process has been tested for three major factors affect the process:
1. Four PVA inhibitor concentrations were selected to be (0, 1000, 4000 and 7000 ppm) in simulated cooling water.
2. Two cathode: anode area ratios as 1:1 and 2.4:1.
3. Two distances apart cathode – anode as 3 and 7 cm.
Maximum corrosion inhibition achieved was 86% which indicates that increa

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Publication Date
Fri Mar 31 2017
Journal Name
Al-khwarizmi Engineering Journal
Corrosion Behavior of Nanocomposite Al-9 wt% Si Alloy Reinforced with Carbon Nanotubes
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An effort is made to study the effect of composite nanocoating using aluminum-9%wt silicon alloys reinforced with different percentage (0.5,1,2,4)wt.% of carbon nanotubes (CNTs) using  plasma spraying. The effect of this composite on corrosion behavior for AA6061-T6 by extrapolation Tafel test in sea water 3.5wt% NaCl was invested. Many specimens where prepared from AA6061-T6 by the dimension (15x15x3)mm as this first set up and other steps include coating process, X-ray diffraction and SEM examination .The results show the CNTs increase the corrosion rate of the nanocomposite coatings with increasing the weight percentage of CNTs within the Al-Si matrix. Al-9wt%Si coating layer itself has less corrosion rate if compared with both n

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Publication Date
Mon Jun 01 2020
Journal Name
Journal Of Engineering
Thermal Modeling of Solar Still Coupled with Heat Pipes and Experimental Validation
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Water is the basis of the existence of all kinds of life, so obtaining it with good quality represents a challenge to human existence and development especially in the desert and remote cities because these areas contain small populations and water purification requires great materials and huge amounts of fossil fuels resulting pollution of the environment. Cheap and environmentally friendly desalination methods have been done by using solar distillations. Passive solar stills have low yields, so in this research, the problem is overcome by connecting four heat pipes which are installed on the parabolic concentrator reflector with passive solar still to increase the temperature of hot water to more than 90°C, as a resul

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Publication Date
Mon Feb 01 2016
Journal Name
International Journal Of Transportation Engineering And Traffic System, Ijtets
Comparative Modeling of Pavement Surface Texture Variables Using ANN and SPSS Software
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The health of Roadway pavement surface is considered as one of the major issues for safe driving. Pavement surface condition is usually referred to micro and macro textures which enhances the friction between the pavement surface and vehicular tires, while it provides a proper drainage for heavy rainfall water. Measurement of the surface texture is not yet standardized, and many different techniques are implemented by various road agencies around the world based on the availability of equipment’s, skilled technicians’ and funds. An attempt has been made in this investigation to model the surface macro texture measured from sand patch method (SPM), and the surface micro texture measured from out flow time (OFT) and British pendul

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Publication Date
Mon Nov 11 2019
Journal Name
Spe
Modeling Rate of Penetration using Artificial Intelligent System and Multiple Regression Analysis
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Abstract<p>Over the years, the prediction of penetration rate (ROP) has played a key rule for drilling engineers due it is effect on the optimization of various parameters that related to substantial cost saving. Many researchers have continually worked to optimize penetration rate. A major issue with most published studies is that there is no simple model currently available to guarantee the ROP prediction.</p><p>The main objective of this study is to further improve ROP prediction using two predictive methods, multiple regression analysis (MRA) and artificial neural networks (ANNs). A field case in SE Iraq was conducted to predict the ROP from a large number of parame</p> ... Show More
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