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Experimental investigation and modelling of residual stresses in face milling of Al-6061-T3 using neural network
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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

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Publication Date
Wed Dec 30 2020
Journal Name
Iraqi Journal Of Chemical And Petroleum Engineering
Experimental Investigation of Pomegranate Peel and Grape Seed Powder Additives on the Rheological and Filtration Properties of Un-Weighted WBM
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   The chemical additives used to enhance the properties of drilling mud cause damage to humans and the environment. Therefore, it is necessary to search for alternative additives to add them to the drilling mud. Thus, this study investigates the effects of pomegranate peel and grape seed powders as natural waste when added to un-weighted water-based mud. The test includes measurements of the rheological properties and filtration, as well as the alkanity and density of the drilling mud. The results showed a decrease in PH values ​​with an increase in the concentrations of pomegranate peel or grapeseed, and a decrease in mud density with an increase in powders of pomegranate peel and grape seed concentrations that resulted f

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Publication Date
Mon Jan 01 2024
Journal Name
Journal Of The Mechanical Behavior Of Materials
Structural behavior of one-way slabs reinforced by a combination of GFRP and steel bars: An experimental and numerical investigation
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Abstract<p>Glass- fiber-reinforced polymer (GFRP) offers a significant alternative to steel in reinforced concrete, with superior corrosion and fire resistance. Though less ductile and more brittle in stress–strain behavior than steel, it is very helpful to combine GFRP with steel reinforcement that improves the structural behavior. This research investigates the flexural characteristics of a one-way slab reinforced by a combination of GFRP and steel reinforcement. Three identical concrete slabs ((1500 × 550 × 120) mm and 43 MPa) were tested under static load with GFRP replacement ratios of (0, 20, and 40)%. The experimental data were utilized to verify a numerical model. The experimen</p> ... Show More
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Publication Date
Wed Sep 01 2021
Journal Name
Iop Conference Series: Earth And Environmental Science
Post Fire Residual Concrete and Steel Reinforcement Properties
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he paper presents the results of exposure of normal concrete to high temperatures (400 and 700°C). In addition to the exposure of steel reinforcement bar Ø 12 mm, where two types of steel reinforcement burning situations were performed. Directly exposed to high temperatures (400 and 700°C) and others were covered by concrete layer (15 mm). From the experimental results of fire exposure for 1 hour of 400 and 700°C and gradually cooled, it was found that the residual average percentage of compressive strength of concrete was 85.3 and 41.4%, while the residual average percentage of modulus of elasticity of concrete was 75 and 48%, respectively. The residual average percentage of yielding tensile stress (Ø 12 mm) after burning and cooling

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Publication Date
Thu Apr 04 2024
Journal Name
Iraqi Journal Of Applied Physics
Experimental Investigation on Mechanical and Physical Properties of Hybrid Plastic/Wood Composites as Echo-friendly Structural and Decorative Materials
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This study focuses on producing wood-plastic composites using unsaturated polyester resin reinforced with Pistacia vera shell particles and wood industry waste powder. Composites with reinforcement ratios of 0%, 20%, 30%, and 40% were prepared and tested for thermal conductivity, impact strength, hardness, and compressive strength. The results revealed that thermal conductivity increases with reinforcement, while maintaining good thermal insulation, reaching a peak value of 0.633453 W/m·K. Hardness decreased with increased reinforcement, reaching a minimum nominal hardness value of 0.9479. Meanwhile, impact strength and compressive strength improved, with peak values of 14.103 k/m² and 57.3864568 MPa, respectively. The main aim is to manu

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Publication Date
Wed Jan 01 2014
Journal Name
International Journal Of Computer Applications
Enhancing the Delta Training Rule for a Single Layer Feedforward Heteroassociative Memory Neural Network
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In this paper, an algorithm is suggested to train a single layer feedforward neural network to function as a heteroassociative memory. This algorithm enhances the ability of the memory to recall the stored patterns when partially described noisy inputs patterns are presented. The algorithm relies on adapting the standard delta rule by introducing new terms, first order term and second order term to it. Results show that the heteroassociative neural network trained with this algorithm perfectly recalls the desired stored pattern when 1.6% and 3.2% special partially described noisy inputs patterns are presented.

Publication Date
Mon Apr 03 2023
Journal Name
International Journal Of Online And Biomedical Engineering (ijoe)
An Integrated Grasshopper Optimization Algorithm with Artificial Neural Network for Trusted Nodes Classification Problem
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Wireless Body Area Network (WBAN) is a tool that improves real-time patient health observation in hospitals, asylums, especially at home. WBAN has grown popularity in recent years due to its critical role and vast range of medical applications. Due to the sensitive nature of the patient information being transmitted through the WBAN network, security is of paramount importance. To guarantee the safe movement of data between sensor nodes and various WBAN networks, a high level of security is required in a WBAN network. This research introduces a novel technique named Integrated Grasshopper Optimization Algorithm with Artificial Neural Network (IGO-ANN) for distinguishing between trusted nodes in WBAN networks by means of a classifica

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Publication Date
Fri Jul 19 2024
Journal Name
An International Journal Of Optimization And Control: Theories &amp; Applications (ijocta)
Design optimal neural network based on new LM training algorithm for solving 3D - PDEs
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In this article, we design an optimal neural network based on new LM training algorithm. The traditional algorithm of LM required high memory, storage and computational overhead because of it required the updated of Hessian approximations in each iteration. The suggested design implemented to converts the original problem into a minimization problem using feed forward type to solve non-linear 3D - PDEs. Also, optimal design is obtained by computing the parameters of learning with highly precise. Examples are provided to portray the efficiency and applicability of this technique. Comparisons with other designs are also conducted to demonstrate the accuracy of the proposed design.

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Publication Date
Mon Apr 01 2019
Journal Name
Materials Chemistry And Physics
Investigation of silica polymorphs stratified in siliceous geode using FTIR and XRD methods
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Publication Date
Sun Jan 05 2025
Journal Name
Science Journal Of University Of Zakho
DETECTION AND RECOGNITION OF IRAQI LICENSE PLATES USING CONVOLUTIONAL NEURAL NETWORKS
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Due to the large population of motorway users in the country of Iraq, various approaches have been adopted to manage queues such as implementation of traffic lights, avoidance of illegal parking, amongst others. However, defaulters are recorded daily, hence the need to develop a mean of identifying these defaulters and bring them to book. This article discusses the development of an approach of recognizing Iraqi licence plates such that defaulters of queue management systems are identified. Multiple agencies worldwide have quickly and widely adopted the recognition of a vehicle license plate technology to expand their ability in investigative and security matters. License plate helps detect the vehicle's information automatically ra

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Publication Date
Tue Jan 01 2019
Journal Name
Association Of Arab Universities Journal Of Engineering Sciences
Experimental and numerical investigation on the behavior of reinforced reactive powder concrete two-way slabs under static load
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This paper studied the behaviour of reinforced reactive powder concrete (RPC) two-way slabs under static load. The experimental program included testing three simply supported slabs of 1000 mm length, 1000 mm width, and 70 mm thickness. Tested specimens were of identical properties except their steel fibers volume ratio (0.5 %, 1 %, and 1.5 %). Static test results revealed that, increasing steel fibers volume ratio from 0.5% to 1% and from 1% to 1.5%, led to an increase in: first crack load by (32.2 % and 52.3 %), ultimate load by (36.1 % and 17.0 %), ultimate deflection by (33.6 % and 3.4 %), absorbed energy by (128 % and 20.2 %), and the ultimate strain by (1.1 % and 6.73 %). The stiffness and ductility of the specimens also increased. A

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